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Burc E, Girard-Tercieux C, Metz M, Cazaux E, Baur J, Koppik M, Rêgo A, Hart AF, Berger D. Life-history adaptation under climate warming magnifies the agricultural footprint of a cosmopolitan insect pest. Nat Commun 2025; 16:827. [PMID: 39827176 PMCID: PMC11743133 DOI: 10.1038/s41467-025-56177-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 01/10/2025] [Indexed: 01/22/2025] Open
Abstract
Climate change is affecting population growth rates of ectothermic pests with potentially dire consequences for agriculture and global food security. However, current projection models of pest impact typically overlook the potential for rapid genetic adaptation, making current forecasts uncertain. Here, we predict how climate change adaptation in life-history traits of insect pests affects their growth rates and impact on agricultural yields by unifying thermodynamics with classic theory on resource acquisition and allocation trade-offs between foraging, reproduction, and maintenance. Our model predicts that warming temperatures will favour resource allocation towards maintenance coupled with increased resource acquisition through larval foraging, and the evolution of this life-history strategy results in both increased population growth rates and per capita host consumption, causing a double-blow on agricultural yields. We find support for these predictions by studying thermal adaptation in life-history traits and gene expression in the wide-spread insect pest, Callosobruchus maculatus; with 5 years of evolution under experimental warming causing an almost two-fold increase in its predicted agricultural footprint. These results show that pest adaptation can offset current projections of agricultural impact and emphasize the need for integrating a mechanistic understanding of life-history evolution into forecasts of pest impact under climate change.
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Affiliation(s)
- Estelle Burc
- Department of Ecology and Genetics, Program of Animal Ecology. Uppsala University, Norbyvägen 18D, 75236, Uppsala, Sweden
- Agronomy Institute Rennes-Angers (IARA), Graduate school of agronomy, 35000, Rennes, France
| | - Camille Girard-Tercieux
- Department of Ecology and Genetics, Program of Animal Ecology. Uppsala University, Norbyvägen 18D, 75236, Uppsala, Sweden
- Université de Toulouse, Toulouse INP-ENSAT, 31326, Castanet-Tolosan, France
- Université de Lorraine, AgroParisTech, INRAE, UMR Silva, 54000, Nancy, France
| | - Moa Metz
- Department of Ecology and Genetics, Program of Animal Ecology. Uppsala University, Norbyvägen 18D, 75236, Uppsala, Sweden
- Department of Biology, Faculty of Natural Sciences, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Elise Cazaux
- Department of Ecology and Genetics, Program of Animal Ecology. Uppsala University, Norbyvägen 18D, 75236, Uppsala, Sweden
- Université de Toulouse, Toulouse INP-ENSAT, 31326, Castanet-Tolosan, France
| | - Julian Baur
- Department of Ecology and Genetics, Program of Animal Ecology. Uppsala University, Norbyvägen 18D, 75236, Uppsala, Sweden
| | - Mareike Koppik
- Department of Ecology and Genetics, Program of Animal Ecology. Uppsala University, Norbyvägen 18D, 75236, Uppsala, Sweden
- Department of Zoology, Animal Ecology, Martin-Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Alexandre Rêgo
- Department of Ecology and Genetics, Program of Animal Ecology. Uppsala University, Norbyvägen 18D, 75236, Uppsala, Sweden
| | - Alex F Hart
- Department of Ecology and Genetics, Program of Animal Ecology. Uppsala University, Norbyvägen 18D, 75236, Uppsala, Sweden
| | - David Berger
- Department of Ecology and Genetics, Program of Animal Ecology. Uppsala University, Norbyvägen 18D, 75236, Uppsala, Sweden.
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2
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Jarne P. The Anthropocene and the biodiversity crisis: an eco-evolutionary perspective. C R Biol 2025; 348:1-20. [PMID: 39780736 DOI: 10.5802/crbiol.172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2024] [Revised: 11/22/2024] [Accepted: 12/09/2024] [Indexed: 01/11/2025]
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3
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Byer NW, Moll RJ, Krynak TJ, Shaffer EE, Brumfield JL, Reinier JE, Eysenbach SR, Cepek JD, Hausman CE. Breeding bird sensitivity to urban habitat quality is multi-scale and strongly dependent on migratory behavior. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2025; 35:e3087. [PMID: 39822037 PMCID: PMC11739834 DOI: 10.1002/eap.3087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2024] [Accepted: 12/04/2024] [Indexed: 01/19/2025]
Abstract
Human-caused conversion of natural habitat areas to developed land cover represents a major driver of habitat loss and fragmentation, leading to reorganization of biological communities. Although protected areas and urban greenspaces can preserve natural systems in fragmented landscapes, their efficacy has been stymied by the complexity and scale-dependency underlying biological communities. While migratory bird communities are easy to-study and particularly responsive to anthropogenic habitat alterations, prior studies have documented substantial variation in habitat sensitivity across species and migratory groups. This may make approaches that explicitly consider the hierarchical nature of ecological organization useful for planning and decision-making, particularly in developed landscapes. Herein, we leverage regional vegetation and breeding bird monitoring efforts to investigate the influences of spatial scale, urbanization, and migratory habit on breeding bird occupancy across Cleveland Metroparks, a large urban park system in Ohio. Using multispecies occupancy models, we found that fine-scale vegetation covariates were more predictive of bird community dynamics than landscape-level covariates, suggesting positive benefits of vegetation management activities for breeding bird communities. We also found that short-distance migrants were positively associated with plants that have broad ecological tolerances and that tropical migrants were more negatively associated with human development than other migratory groups. While local vegetation management may be effective for protecting sensitive breeding bird communities, many tropical migrants required intact forests with low human development and may require targeted habitat management for continued breeding-season occupancy. More broadly, this study emphasizes how avian management strategies in developed landscapes should consider features at multiple spatial scales-as well as species-specific migratory behaviors.
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Affiliation(s)
- Nathan W. Byer
- Division of Natural Resources, Park Operations DepartmentCleveland MetroparksClevelandOhioUSA
| | - Remington J. Moll
- Department of Natural Resources and the EnvironmentCollege of Life Sciences and Agriculture, University of New HampshireDurhamNew HampshireUSA
| | - Timothy J. Krynak
- Division of Natural Resources, Park Operations DepartmentCleveland MetroparksClevelandOhioUSA
| | - Erik E. Shaffer
- Division of Natural Resources, Park Operations DepartmentCleveland MetroparksClevelandOhioUSA
| | - Jen L. Brumfield
- Division of Natural Resources, Park Operations DepartmentCleveland MetroparksClevelandOhioUSA
| | - John E. Reinier
- Division of Natural Resources, Park Operations DepartmentCleveland MetroparksClevelandOhioUSA
| | - Sarah R. Eysenbach
- Division of Natural Resources, Park Operations DepartmentCleveland MetroparksClevelandOhioUSA
| | - Jonathon D. Cepek
- Division of Natural Resources, Park Operations DepartmentCleveland MetroparksClevelandOhioUSA
| | - Constance E. Hausman
- Division of Natural Resources, Park Operations DepartmentCleveland MetroparksClevelandOhioUSA
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4
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Dejeante R, Lemaire‐Patin R, Chamaillé‐Jammes S. How Can Overlooking Social Interactions, Space Familiarity or Other "Invisible Landscapes" Shaping Animal Movement Bias Habitat Selection Estimations and Species Distribution Predictions? Ecol Evol 2025; 15:e70782. [PMID: 39781261 PMCID: PMC11707625 DOI: 10.1002/ece3.70782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2024] [Revised: 12/03/2024] [Accepted: 12/15/2024] [Indexed: 01/12/2025] Open
Abstract
Species' future distributions are commonly predicted using models that link the likelihood of occurrence of individuals to the environment. Although animals' movements are influenced by physical and non-physical landscapes, for example related to individual experiences such as space familiarity or previous encounters with conspecifics, species distribution models developed from observations of unknown individuals cannot integrate these latter variables, turning them into 'invisible landscapes'. In this theoretical study, we address how overlooking 'invisible landscapes' impacts the estimation of habitat selection and thereby the projection of future distributions. Overlooking the attraction towards some 'invisible' variable consistently led to overestimating the strength of habitat selection. Consequently, projections of future population distributions were also biased, with animals following changes in preferred habitat less than predicted. Our results reveal an overlooked challenge faced by correlative species distribution models based on the observation of unknown individuals, whose past experience of the environment is by definition not known. Mechanistic distribution modeling integrating cognitive processes underlying movement should be developed.
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Affiliation(s)
| | | | - Simon Chamaillé‐Jammes
- CEFEUniv Montpellier, CNRS, EPHE, IRDMontpellierFrance
- Department of Zoology and EntomologyMammal Research Institute, University of PretoriaPretoriaSouth Africa
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5
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Pollack L, Culshaw‐Maurer M, Sih A. Social dominance influences individual susceptibility to an evolutionary trap in mosquitofish. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2025; 35:e3081. [PMID: 39829287 PMCID: PMC11744343 DOI: 10.1002/eap.3081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2024] [Accepted: 10/10/2024] [Indexed: 01/22/2025]
Abstract
Plastic pollution threatens almost every ecosystem in the world. Critically, many animals consume plastic, in part because plastic particles often look or smell like food. Plastic ingestion is thus an evolutionary trap, a phenomenon that occurs when cues are decoupled from their previously associated high fitness outcomes. Theory predicts that dominance hierarchies could dictate individual responses to evolutionary traps across social environments, but the social dimension of evolutionary trap responses has rarely been investigated. We tested how variation in group size influences the formation of dominance relationships and, in turn, how these dominance relationships drive differences in foraging behavior in Western mosquitofish (Gambusia affinis). This included foraging for a variety of familiar and novel food-like items, including microplastics. Overall, dominant individuals were often the first to sample food and had higher bite rates than subordinates, including when foraging for microplastics. Importantly, how dominance affected foraging behavior depended on group size and on whether groups were presented with familiar or novel foods. Furthermore, individuals were consistent in their foraging behavior across trials with different group sizes, indicating the formation of stable social roles. These results suggest that predicting the ecological and evolutionary consequences of evolutionary traps will require an understanding of how social structures influence trap susceptibility.
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Affiliation(s)
- Lea Pollack
- Department of Environmental Science and PolicyUniversity of California, DavisDavisCaliforniaUSA
- Department of Ecology, Evolution and BehaviorUniversity of MinnesotaSaint PaulMinnesotaUSA
| | - Michael Culshaw‐Maurer
- Department of Ecology and EvolutionUniversity of California, DavisDavisCaliforniaUSA
- Metro TransitMinneapolisMinnesotaUSA
| | - Andrew Sih
- Department of Environmental Science and PolicyUniversity of California, DavisDavisCaliforniaUSA
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6
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Vollert SA, Drovandi C, Adams MP. Ecosystem Knowledge Should Replace Coexistence and Stability Assumptions in Ecological Network Modelling. Bull Math Biol 2024; 87:17. [PMID: 39739139 DOI: 10.1007/s11538-024-01407-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2024] [Accepted: 12/20/2024] [Indexed: 01/02/2025]
Abstract
Quantitative population modelling is an invaluable tool for identifying the cascading effects of conservation on an ecosystem. When population data from monitoring programs is not available, deterministic ecosystem models have often been calibrated using the theoretical assumption that ecosystems have a stable, coexisting equilibrium. However, a growing body of literature suggests these theoretical assumptions are inappropriate for conservation contexts. Here, we develop an alternative for data-free population modelling that relies on expert-elicited knowledge of species populations. Our new Bayesian algorithm systematically removes model parameters that lead to impossible predictions, as defined by experts, without incurring excessive computational costs. We demonstrate our framework on an ordinary differential equation model by limiting predicted population sizes and their ability to change rapidly, utilising readily available knowledge from field observations and experts rather than relying on theoretical ecosystem properties. Our results show that using only coexistence and stability requirements can lead to unrealistic population dynamics, which can be avoided by switching to expert-derived information. We demonstrate how this change can dramatically impact population predictions, expected responses to management, conservation decision-making, and long-term ecosystem behaviour. Without data, we argue that field observations and expert knowledge are more trustworthy for representing ecosystems observed in nature, improving the precision and confidence in predictions.
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Affiliation(s)
- Sarah A Vollert
- Centre for Data Science, Queensland University of Technology, Brisbane, 4000, Australia.
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, 4000, Australia.
| | - Christopher Drovandi
- Centre for Data Science, Queensland University of Technology, Brisbane, 4000, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, 4000, Australia
| | - Matthew P Adams
- Centre for Data Science, Queensland University of Technology, Brisbane, 4000, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, 4000, Australia
- School of Chemical Engineering, The University of Queensland, St Lucia, 4067, Australia
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7
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Chambert T, Barbraud C, Cam E, Chabrolle A, Sadoul N, Besnard A. A modeling approach to forecast local demographic trends in metapopulations. Ecology 2024:e4459. [PMID: 39496481 DOI: 10.1002/ecy.4459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 07/18/2024] [Accepted: 08/29/2024] [Indexed: 11/06/2024]
Abstract
Predicting animal population trajectories into the future has become a central exercise in both applied and fundamental ecology. Because demographic models classically assume population closure, they tend to provide inaccurate predictions when applied locally to interconnected subpopulations that are part of a larger metapopulation. Ideally, one should explicitly model dispersal among subpopulations, but in practice this is prevented by the difficulty of estimating dispersal rates in the wild. To forecast the local demography of connected subpopulations, we developed a new demographic model (hereafter, the two-scale model) that disentangles two processes occurring at different spatial scales. First, at the larger scale, a closed population model describes changes in metapopulation size over time. Second, total metapopulation size is redistributed among subpopulations, using time-varying proportionality parameters. This two-step approach ensures that the long-term growth of every subpopulation is constrained by the overall metapopulation growth rate. It implicitly accounts for the interconnectedness among subpopulations and avoids unrealistic trajectories. Using realistic simulations, we compared the performance of this new model with that of a classical closed population model at predicting subpopulations' trajectories over 30 years. While the classical model predicted future subpopulation sizes with an average bias of 30% and produced predictive errors sometimes >500%, the two-scale model showed very little bias (<3%) and never produced predictive errors >20%. We also applied both models to a real dataset on European shags (Gulosus aristotelis) breeding along the Atlantic coast of France. Again, the classical model predicted highly unrealistic growths, as large as a 200-fold increase over 30 years for some subpopulations. The two-scale model predicted very sensible growths, never larger than a threefold increase over the 30-year time horizon, which is more in accordance with this species' life history. This two-scale model provides an effective solution to forecast the local demography of connected subpopulations in the absence of data on dispersal rates. In this context, it is a better alternative than closed population models and a more parsimonious option than full-dispersal models. Because the only data required are simple counts, this model could be useful to many large-scale wildlife monitoring programs.
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Affiliation(s)
- Thierry Chambert
- CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France
| | - Christophe Barbraud
- Centre d'Etudes Biologiques de Chizé, UMR7372 CNRS-La Rochelle Université, Villiers-en-Bois, France
| | - Emmanuelle Cam
- Laboratoire des Sciences de l'Environnement Marin, LEMAR UMR 6539 CNRS/UBO/IRD/Ifremer, Université de Bretagne Occidentale, Institut Universitaire Européen de la Mer, Plouzané, France
| | - Antoine Chabrolle
- Centre d'Ecologie et des Sciences de la Conservation (CESCO), Muséum National d'Histoire Naturelle, Station de Biologie Marine, Concarneau, France
| | - Nicolas Sadoul
- Groupement d'intérêt Scientifique Oiseaux Marins (GISOM), Station de Biologie Marine, Concarneau, France
| | - Aurélien Besnard
- CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France
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8
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Wesselkamp M, Moser N, Kalweit M, Boedecker J, Dormann CF. Process-Informed Neural Networks: A Hybrid Modelling Approach to Improve Predictive Performance and Inference of Neural Networks in Ecology and Beyond. Ecol Lett 2024; 27:e70012. [PMID: 39625058 PMCID: PMC11613309 DOI: 10.1111/ele.70012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 10/14/2024] [Accepted: 10/16/2024] [Indexed: 12/06/2024]
Abstract
Despite deep learning being state of the art for data-driven model predictions, its application in ecology is currently subject to two important constraints: (i) deep-learning methods are powerful in data-rich regimes, but in ecology data are typically sparse; and (ii) deep-learning models are black-box methods and inferring the processes they represent are non-trivial to elicit. Process-based (= mechanistic) models are not constrained by data sparsity or unclear processes and are thus important for building up our ecological knowledge and transfer to applications. In this work, we combine process-based models and neural networks into process-informed neural networks (PINNs), which incorporate the process knowledge directly into the neural network structure. In a systematic evaluation of spatial and temporal prediction tasks for C-fluxes in temperate forests, we show the ability of five different types of PINNs (i) to outperform process-based models and neural networks, especially in data-sparse regimes with high-transfer task and (ii) to inform on mis- or undetected processes.
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Affiliation(s)
- Marieke Wesselkamp
- Biometry and Environmental System AnalysisUniversity of FreiburgFreiburg im BreisgauGermany
| | - Niklas Moser
- Biometry and Environmental System AnalysisUniversity of FreiburgFreiburg im BreisgauGermany
- Biological and Environmental ScienceUniversity of JyväskyläJyväskyläFinland
| | - Maria Kalweit
- Department of Computer ScienceUniversity of FreiburgFreiburg im BreisgauGermany
| | - Joschka Boedecker
- Department of Computer ScienceUniversity of FreiburgFreiburg im BreisgauGermany
- Cluster of Excellence BrainLinks‐BrainToolsFreiburg im BreisgauGermany
| | - Carsten F. Dormann
- Biometry and Environmental System AnalysisUniversity of FreiburgFreiburg im BreisgauGermany
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9
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Briscoe Runquist R, Moeller DA. Isolation by environment and its consequences for range shifts with global change: Landscape genomics of the invasive plant common tansy. Mol Ecol 2024; 33:e17462. [PMID: 38993027 DOI: 10.1111/mec.17462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/29/2024] [Accepted: 05/30/2024] [Indexed: 07/13/2024]
Abstract
Invasive species are a growing global economic and ecological problem. However, it is not well understood how environmental factors mediate invasive range expansion. In this study, we investigated the recent and rapid range expansion of common tansy across environmental gradients in Minnesota, USA. We densely sampled individuals across the expanding range and performed reduced representation sequencing to generate a dataset of 3071 polymorphic loci for 176 individuals. We used non-spatial and spatially explicit analyses to determine the relative influences of geographic distance and environmental variation on patterns of genomic variation. We found no evidence for isolation by distance but strong evidence for isolation by environment, indicating that environmental factors may have modulated patterns of range expansion. Land use classification and soils were particularly important variables related to population structure although they operated on different spatial scales; land use classification was related to broad-scale patterns and soils were related to fine-scale patterns. All analyses indicated a distinctive genetic cluster in the most recently invaded portion of the range. Individuals from the far northwestern range margin were separated from the remainder of the range by reduced migration, which was associated with environmental resistance. This portion of the range was invaded primarily in the last 15 years. Ecological niche models also indicated that this cluster was associated with the expansion of the niche. While invasion is often assumed to be primarily influenced by dispersal limitation, our results suggest that ongoing invasion and range shifts with climate change may be strongly affected by environmental heterogeneity.
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Affiliation(s)
- Ryan Briscoe Runquist
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota, USA
| | - David A Moeller
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota, USA
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10
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Bozzuto C, Ives AR. Predictability of ecological and evolutionary dynamics in a changing world. Proc Biol Sci 2024; 291:20240980. [PMID: 38981521 PMCID: PMC11335013 DOI: 10.1098/rspb.2024.0980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 06/07/2024] [Accepted: 06/07/2024] [Indexed: 07/11/2024] Open
Abstract
Ecological and evolutionary predictions are being increasingly employed to inform decision-makers confronted with intensifying pressures on biodiversity. For these efforts to effectively guide conservation actions, knowing the limit of predictability is pivotal. In this study, we provide realistic expectations for the enterprise of predicting changes in ecological and evolutionary observations through time. We begin with an intuitive explanation of predictability (the extent to which predictions are possible) employing an easy-to-use metric, predictive power PP(t). To illustrate the challenge of forecasting, we then show that among insects, birds, fishes and mammals, (i) 50% of the populations are predictable at most 1 year in advance and (ii) the median 1-year-ahead predictive power corresponds to a prediction R 2 of only 20%. Predictability is not an immutable property of ecological systems. For example, different harvesting strategies can impact the predictability of exploited populations to varying degrees. Moreover, incorporating explanatory variables, accounting for time trends and considering multivariate time series can enhance predictability. To effectively address the challenge of biodiversity loss, researchers and practitioners must be aware of the information within the available data that can be used for prediction and explore efficient ways to leverage this knowledge for environmental stewardship.
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Affiliation(s)
- Claudio Bozzuto
- Wildlife Analysis GmbH, Oetlisbergstrasse 38, 8053 Zurich, Switzerland
| | - Anthony R. Ives
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI53706, USA
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11
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Poddar U, Lam K, Gurevitch J. Trends in research approaches and gender in plant ecology dissertations over four decades. Ecol Evol 2024; 14:e11554. [PMID: 38863722 PMCID: PMC11165400 DOI: 10.1002/ece3.11554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 05/20/2024] [Accepted: 05/26/2024] [Indexed: 06/13/2024] Open
Abstract
Dissertations are a foundational scientific product; they are the formative product that early-career scientists create and share original knowledge. The methodological approaches used in dissertations vary with the research field. In plant ecology, these approaches include observations, experiments (field or controlled environment), literature reviews, theoretical approaches, or analyses of existing data (including "big data"). Recently, concerns have been raised about the rise of "big data" studies and the loss of observational and field-based studies in ecology, but such trends have not been formally quantified. Therefore, we examined how the emphasis on each of these categories has changed over time and whether male and female authors differ in the methods employed. We found remarkable temporal consistency, with observational studies being dominant over the entire time span examined. There was an increase in the number of approaches employed per dissertation, with increases in analyses of databases and theoretical studies adding to rather than replacing traditional methodologies (like observations and field experiments). The representation of women increased over time. There were some differences in the approaches taken by men and women, which requires further investigation.
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Affiliation(s)
- Urmi Poddar
- Department of Ecology and EvolutionStony Brook UniversityStony BrookNew YorkUSA
| | - Kristi Lam
- Department of Ecology and EvolutionStony Brook UniversityStony BrookNew YorkUSA
- Roslyn High SchoolRoslyn HeightsNew YorkUSA
| | - Jessica Gurevitch
- Department of Ecology and EvolutionStony Brook UniversityStony BrookNew YorkUSA
- Department of Forestry and Natural ResourcesPurdue UniversityWest LafayetteIndianaUSA
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12
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Ohlmann M, Munoz F, Massol F, Thuiller W. Assessing mutualistic metacommunity capacity by integrating spatial and interaction networks. Theor Popul Biol 2024; 156:22-39. [PMID: 38219873 DOI: 10.1016/j.tpb.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 12/26/2023] [Accepted: 01/10/2024] [Indexed: 01/16/2024]
Abstract
We develop a spatially realistic model of mutualistic metacommunities that exploits the joint structure of spatial and interaction networks. Assuming that all species have the same colonisation and extinction parameters, this model exhibits a sharp transition between stable non-null equilibrium states and a global extinction state. This behaviour allows defining a threshold on colonisation/extinction parameters for the long-term metacommunity persistence. This threshold, the 'metacommunity capacity', extends the metapopulation capacity concept and can be calculated from the spatial and interaction networks without needing to simulate the whole dynamics. In several applications we illustrate how the joint structure of the spatial and the interaction networks affects metacommunity capacity. It results that a weakly modular spatial network and a power-law degree distribution of the interaction network provide the most favourable configuration for the long-term persistence of a mutualistic metacommunity. Our model that encodes several explicit ecological assumptions should pave the way for a larger exploration of spatially realistic metacommunity models involving multiple interaction types.
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Affiliation(s)
- Marc Ohlmann
- Univ. Grenoble Alpes, CNRS, Univ. Savoie Mont-Blanc, LECA, Laboratoire d'Ecologie Alpine, F-38000 Grenoble, France
| | - François Munoz
- Univ. Grenoble Alpes, CNRS, Univ. Savoie Mont-Blanc, LECA, Laboratoire d'Ecologie Alpine, F-38000 Grenoble, France; Univ. Grenoble Alpes, CNRS, Liphy, Laboratoire Interdisciplinaire de Physique, F-38000 Grenoble, France
| | - François Massol
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019 - UMR 9017 - CIIL - Center for Infection and Immunity of Lille, F-59000 Lille, France
| | - Wilfried Thuiller
- Univ. Grenoble Alpes, CNRS, Univ. Savoie Mont-Blanc, LECA, Laboratoire d'Ecologie Alpine, F-38000 Grenoble, France.
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13
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Aldabe J, Morán-López T, Soca P, Blumetto O, Morales JM. Bird species responses to rangeland management in relation to their traits: Rio de la Plata Grasslands as a case study. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2024; 34:e2933. [PMID: 37983735 DOI: 10.1002/eap.2933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/31/2023] [Accepted: 10/04/2023] [Indexed: 11/22/2023]
Abstract
Areas used for livestock production and dominated by native grasses represent a unique opportunity to reconcile biodiversity conservation and livestock production. However, limited knowledge of individual species' responses to rangeland management restricts our capacity to design grazing practices that favor endangered species and other priority birds. In this work, we applied Hierarchical Modelling of Species Communities (HMSC) to study individual species responses, as well as the influence of traits on such responses, to variables related to rangeland management using birds of the Rio de la Plata Grasslands as a case study. Based on presence-absence data collected in 454 paddocks across 46 ranches we inferred the response of 69 species considering imperfect detection. This degree of detail fills a major gap in rangeland management, as species-level responses can be used to achieve targeted conservation goals other than maximizing richness or abundance. We found that artificial pastures had an overall negative impact on many bird species, whereas the presence of tussocks had a positive effect, including all threatened species. Grassland specialists were in general sensitive to grass height and tended to respond positively to tussocks but negatively to tree cover. Controlling grass height via adjustments in stocking rate can be a useful tool to favor grassland specialists. To favor a wide range of bird species in ranches, a mosaic of short and tall native grasslands with patches of tussocks and trees is desirable. We also found that species-specific responses were modulated by their traits: small-sized birds responded positively to tussocks and tree cover while large species responded negatively to increasing grass height. Ground foragers preferred short grass while birds that scarcely use this stratum were not affected by grass height. Results on the influence of traits on bird responses are an important novelty in relation to previous work in rangelands and potentially increase our predicting capacity and model transferability across grassland regions.
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Affiliation(s)
- Joaquín Aldabe
- Departamento de Sistemas Agrarios y Paisajes Culturales, Centro Universitario Regional del Este, Universidad de la República, Rocha, Uruguay
- Southern Cone Grassland Alliance, Aves Uruguay-BirdLife International, Montevideo, Uruguay
| | - Teresa Morán-López
- Departamento de Biología de Organismos y Sistemas, Universidad de Oviedo and Instituto Mixto de Investigación en Biodiversidad (Universidad de Oviedo-CSIC-Principado de Asturias), Oviedo y Mieres, Spain
- Grupo de Ecología Cuantitativa, INIBIOMA-CONICET, Universidad Nacional del Comahue, Bariloche, Argentina
| | - Pablo Soca
- Ecología del Pastoreo Group, Departamento de Producción Animal y Pasturas, Facultad de Agronomía, Universidad de la República, Montevideo, Uruguay
| | - Oscar Blumetto
- Instituto Nacional de Investigación Agropecuaria (INIA). Area de Recursos Naturales, Producción y Ambiente. Estación Experimental INIA Las Brujas, Canelones, Uruguay
| | - Juan Manuel Morales
- Grupo de Ecología Cuantitativa, INIBIOMA-CONICET, Universidad Nacional del Comahue, Bariloche, Argentina
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
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14
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Litmer AR, Beaupre SJ. Thermal sensitivity of digestion in Sceloporus consobrinus, with comments on geographic variation. J Therm Biol 2024; 120:103808. [PMID: 38387224 DOI: 10.1016/j.jtherbio.2024.103808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 01/22/2024] [Accepted: 01/22/2024] [Indexed: 02/24/2024]
Abstract
Individual variation in energetics, environment, and genetics can influence population-level processes. However, it is often assumed that locally measured thermal and bioenergetic responses apply among broadly related species. Even closely related taxa may differ in the thermal sensitivity of performance, which in turn influences population persistence, population vital rates, and the ability to respond to environmental changes. The objectives of this project were to quantify the thermal sensitivity of digestive physiology in an Sceloporus lizards, to compare closely related, but geographically distinct, populations. Sceloporus lizards are a model organism, as they are known to exhibit thermally dependent physiologies and are geographically widespread. Digestive passage time, food consumption, fecal and urate production, metabolizable energy intake (MEI), and assimilated energy (AE) were compared for Sceloporus consobrinus in Arkansas and S. undulatus in South Carolina and New Jersey. Published data were acquired for NJ and SC lizards, while original data were collected for S. consobrinus. Comparisons of digestion among populations were made at 30 °C, 33 °C, or 36 °C. Results suggest that digestive physiology differs among populations, with S. consobrinus being more efficient at warmer temperatures. In contrast, NJ and SC lizards had quicker passage times and lower fecal and urate production at 30 °C in comparison to AR. The results of the current study exemplify how closely related organisms can differ in thermal sensitivity of performance. Such data are important for understanding how individual-level processes can vary in response to climate, with implications for understanding variation in physiological traits across the range of Sceloporus lizards.
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Affiliation(s)
- Allison R Litmer
- University of Arkansas, Department of Biological Sciences, 650 W. Dickson Street, Fayetteville, AR, 72701, USA.
| | - Steven J Beaupre
- University of Arkansas, Department of Biological Sciences, 650 W. Dickson Street, Fayetteville, AR, 72701, USA
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15
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D'Andrea R, Khattar G, Koffel T, Frans VF, Bittleston LS, Cuellar-Gempeler C. Reciprocal inhibition and competitive hierarchy cause negative biodiversity-ecosystem function relationships. Ecol Lett 2024; 27:e14356. [PMID: 38193391 DOI: 10.1111/ele.14356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/02/2023] [Accepted: 11/29/2023] [Indexed: 01/10/2024]
Abstract
The relationship between biodiversity and ecosystem function (BEF) captivates ecologists, but the factors responsible for the direction of this relationship remain unclear. While higher ecosystem functioning at higher biodiversity levels ('positive BEF') is not universal in nature, negative BEF relationships seem puzzlingly rare. Here, we develop a dynamical consumer-resource model inspired by microbial decomposer communities in pitcher plant leaves to investigate BEF. We manipulate microbial diversity via controlled colonization and measure their function as total ammonia production. We test how niche partitioning among bacteria and other ecological processes influence BEF in the leaves. We find that a negative BEF can emerge from reciprocal interspecific inhibition in ammonia production causing a negative complementarity effect, or from competitive hierarchies causing a negative selection effect. Absent these factors, a positive BEF was the typical outcome. Our findings provide a potential explanation for the rarity of negative BEF in empirical data.
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Affiliation(s)
- Rafael D'Andrea
- Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York, USA
| | - Gabriel Khattar
- Department of Biology, Concordia University, Montreal, Quebec, Canada
| | - Thomas Koffel
- Laboratoire de Biométrie et Biologie Evolutive UMR5558, Université de Lyon, Université Lyon 1, CNRS, Villeurbanne, France
| | - Veronica F Frans
- Department of Fisheries and Wildlife, Center for Systems Integration and Sustainability, Michigan State University, East Lansing, Michigan, USA
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
- W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, Michigan, USA
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16
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Bahlai CA. Forecasting insect dynamics in a changing world. CURRENT OPINION IN INSECT SCIENCE 2023; 60:101133. [PMID: 37858790 DOI: 10.1016/j.cois.2023.101133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 10/04/2023] [Accepted: 10/13/2023] [Indexed: 10/21/2023]
Abstract
Predicting how insects will respond to stressors through time is difficult because of the diversity of insects, environments, and approaches used to monitor and model. Forecasting models take correlative/statistical, mechanistic models, and integrated forms; in some cases, temporal processes can be inferred from spatial models. Because of heterogeneity associated with broad community measurements, models are often unable to identify mechanistic explanations. Many present efforts to forecast insect dynamics are restricted to single-species models, which can offer precise predictions but limited generalizability. Trait-based approaches may offer a good compromise that limits the masking of the ranges of responses while still offering insight. Regardless of the modeling approach, the data used to parameterize a forecasting model should be carefully evaluated for temporal autocorrelation, minimum data needs, and sampling biases in the data. Forecasting models can be tested using near-term predictions and revised to improve future forecasts.
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Affiliation(s)
- Christie A Bahlai
- Department of Biological Sciences, Kent State University, Kent, OH 44242, USA; Environmental Science and Design Research Institute, Kent State University, Kent, OH 44242, USA.
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17
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Henniger H, Huth A, Bohn FJ. A new approach to derive productivity of tropical forests using radar remote sensing measurements. ROYAL SOCIETY OPEN SCIENCE 2023; 10:231186. [PMID: 38026043 PMCID: PMC10663792 DOI: 10.1098/rsos.231186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023]
Abstract
Deriving gross & net primary productivity (GPP & NPP) and carbon turnover time of forests from remote sensing remains challenging. This study presents a novel approach to estimate forest productivity by combining radar remote sensing measurements, machine learning and an individual-based forest model. In this study, we analyse the role of different spatial resolutions on predictions in the context of the Radar BIOMASS mission (by ESA). In our analysis, we use the forest gap model FORMIND in combination with a boosted regression tree (BRT) to explore how spatial biomass distributions can be used to predict GPP, NPP and carbon turnover time (τ) at different resolutions. We simulate different spatial biomass resolutions (4 ha, 1 ha and 0.04 ha) in combination with different vertical resolutions (20, 10 and 2 m). Additionally, we analysed the robustness of this approach and applied it to disturbed and mature forests. Disturbed forests have a strong influence on the predictions which leads to high correlations (R2 > 0.8) at the spatial scale of 4 ha and 1 ha. Increased vertical resolution leads generally to better predictions for productivity (GPP & NPP). Increasing spatial resolution leads to better predictions for mature forests and lower correlations for disturbed forests. Our results emphasize the value of the forthcoming BIOMASS satellite mission and highlight the potential of deriving estimates for forest productivity from information on forest structure. If applied to more and larger areas, the approach might ultimately contribute to a better understanding of forest ecosystems.
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Affiliation(s)
- Hans Henniger
- Department of Ecological Modeling, Helmholtz Centre of Environmental Research (UFZ), Permoserstraße 15, Leipzig 04318, Germany
- Institute for Environmental Systems Research, University of Osnabrück, Barbara Straße 12, Osnabrück 49074, Germany
| | - Andreas Huth
- Department of Ecological Modeling, Helmholtz Centre of Environmental Research (UFZ), Permoserstraße 15, Leipzig 04318, Germany
- Institute for Environmental Systems Research, University of Osnabrück, Barbara Straße 12, Osnabrück 49074, Germany
- iDiv German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig, Puschstraße 4, Leipzig 04103, Germany
| | - Friedrich J. Bohn
- Department of Computational Hydrosystems, Helmholtz Centre of Environmental Research (UFZ), Permoserstraße 15, Leipzig 04318, Germany
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18
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de Koning K, Broekhuijsen J, Kühn I, Ovaskainen O, Taubert F, Endresen D, Schigel D, Grimm V. Digital twins: dynamic model-data fusion for ecology. Trends Ecol Evol 2023; 38:916-926. [PMID: 37208222 DOI: 10.1016/j.tree.2023.04.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/21/2023]
Abstract
Digital twins (DTs) are an emerging phenomenon in the public and private sectors as a new tool to monitor and understand systems and processes. DTs have the potential to change the status quo in ecology as part of its digital transformation. However, it is important to avoid misguided developments by managing expectations about DTs. We stress that DTs are not just big models of everything, containing big data and machine learning. Rather, the strength of DTs is in combining data, models, and domain knowledge, and their continuous alignment with the real world. We suggest that researchers and stakeholders exercise caution in DT development, keeping in mind that many of the strengths and challenges of computational modelling in ecology also apply to DTs.
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Affiliation(s)
- Koen de Koning
- Wageningen University and Research, Environmental Systems Analysis Group, P.O. Box 47, 6700, AA, Wageningen, The Netherlands
| | - Jeroen Broekhuijsen
- Nederlandse organisatie voor toegepast natuurwetenschappenlijk onderzoek - TNO, Department of Monitoring & Control Services, Eemsgolaan 3, 9727 DW Groningen, The Netherlands
| | - Ingolf Kühn
- Helmholtz Centre for Environmental Research - UFZ, Department of Community Ecology, Theodor-Lieser-Strasse, 4, 06120 Halle, Germany; Martin Luther University Halle-Wittenberg, Institute for Biology/Geobotany & Botanical Garden, Große Steinstraße 79/80, 06108 Halle, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstrasse 4, 04103 Leipzig, Germany
| | - Otso Ovaskainen
- Department of Biological and Environmental Science, University of Jyväskylä, P.O. Box 35 (Survontie 9C), FI-40014 Jyväskylä, Finland; Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, P.O. Box 65, Helsinki 00014, Finland; Department of Biology, Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim N-7491, Norway
| | - Franziska Taubert
- Helmholtz Centre for Environmental Research - UFZ, Department of Ecological Modelling, Permoserstr. 15, 04318 Leipzig, Germany
| | - Dag Endresen
- University of Oslo, Natural History Museum, Sars gate 1, NO-0562 Oslo, Norway.
| | - Dmitry Schigel
- Global Biodiversity Information Facility - GBIF Secreteriat, Universitetsparken 15, DK-2100 Copenhagen Ø, Denmark
| | - Volker Grimm
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstrasse 4, 04103 Leipzig, Germany; Helmholtz Centre for Environmental Research - UFZ, Department of Ecological Modelling, Permoserstr. 15, 04318 Leipzig, Germany; University of Potsdam, Plant Ecology and Nature Conservation, Am Mühlenberg 3, 14476 Potsdam, Germany
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19
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Fronhofer EA, Corenblit D, Deshpande JN, Govaert L, Huneman P, Viard F, Jarne P, Puijalon S. Eco-evolution from deep time to contemporary dynamics: The role of timescales and rate modulators. Ecol Lett 2023; 26 Suppl 1:S91-S108. [PMID: 37840024 DOI: 10.1111/ele.14222] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 03/27/2023] [Accepted: 03/29/2023] [Indexed: 10/17/2023]
Abstract
Eco-evolutionary dynamics, or eco-evolution for short, are often thought to involve rapid demography (ecology) and equally rapid heritable phenotypic changes (evolution) leading to novel, emergent system behaviours. We argue that this focus on contemporary dynamics is too narrow: Eco-evolution should be extended, first, beyond pure demography to include all environmental dimensions and, second, to include slow eco-evolution which unfolds over thousands or millions of years. This extension allows us to conceptualise biological systems as occupying a two-dimensional time space along axes that capture the speed of ecology and evolution. Using Hutchinson's analogy: Time is the 'theatre' in which ecology and evolution are two interacting 'players'. Eco-evolutionary systems are therefore dynamic: We identify modulators of ecological and evolutionary rates, like temperature or sensitivity to mutation, which can change the speed of ecology and evolution, and hence impact eco-evolution. Environmental change may synchronise the speed of ecology and evolution via these rate modulators, increasing the occurrence of eco-evolution and emergent system behaviours. This represents substantial challenges for prediction, especially in the context of global change. Our perspective attempts to integrate ecology and evolution across disciplines, from gene-regulatory networks to geomorphology and across timescales, from today to deep time.
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Affiliation(s)
| | - Dov Corenblit
- GEOLAB, Université Clermont Auvergne, CNRS, Clermont-Ferrand, France
- Laboratoire écologie fonctionnelle et environnement, Université Paul Sabatier, CNRS, INPT, UPS, Toulouse, France
| | | | - Lynn Govaert
- Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
| | - Philippe Huneman
- Institut d'Histoire et de Philosophie des Sciences et des Techniques (CNRS/Université Paris I Sorbonne), Paris, France
| | - Frédérique Viard
- ISEM, Université de Montpellier, CNRS, IRD, EPHE, Montpellier, France
| | - Philippe Jarne
- CEFE, UMR 5175, CNRS - Université de Montpellier - Université Paul-Valéry Montpellier - IRD - EPHE, Montpellier Cedex 5, France
| | - Sara Puijalon
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR 5023 LEHNA, Villeurbanne, France
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20
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Bodensteiner BL, Iverson JB, Lea CA, Milne-Zelman CL, Mitchell TS, Refsnider JM, Voves K, Warner DA, Janzen FJ. Mother knows best: nest-site choice homogenizes embryo thermal environments among populations in a widespread ectotherm. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220155. [PMID: 37427473 PMCID: PMC10331915 DOI: 10.1098/rstb.2022.0155] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 05/02/2023] [Indexed: 07/11/2023] Open
Abstract
Species with large geographical ranges provide an excellent model for studying how different populations respond to dissimilar local conditions, particularly with respect to variation in climate. Maternal effects, such as nest-site choice greatly affect offspring phenotypes and survival. Thus, maternal behaviour has the potential to mitigate the effects of divergent climatic conditions across a species' range. We delineated natural nesting areas of six populations of painted turtles (Chrysemys picta) that span a broad latitudinal range and quantified spatial and temporal variation in nest characteristics. To quantify microhabitats available for females to choose, we also identified sites within the nesting area of each location that were representative of available thermal microhabitats. Across the range, females nested non-randomly and targeted microhabitats that generally had less canopy cover and thus higher nest temperatures. Nest microhabitats differed among locations but did not predictably vary with latitude or historic mean air temperature during embryonic development. In conjunction with other studies of these populations, our results suggest that nest-site choice is homogenizing nest environments, which buffers embryos from thermally induced selection and could slow embryonic evolution. Thus, although effective at a macroclimatic scale, nest-site choice is unlikely to compensate for novel stressors that rapidly increase local temperatures. This article is part of the theme issue 'The evolutionary ecology of nests: a cross-taxon approach'.
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Affiliation(s)
- Brooke L. Bodensteiner
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USA
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
| | - John B. Iverson
- Department of Biology, Earlham College, Richmond, IN 60071, USA
| | - Carter A. Lea
- Office of Research Proposal Development, Tulane University, New Orleans, LA 70118, USA
| | | | - Timothy S. Mitchell
- College of Biological Sciences, University of Minnesota, St. Paul, MN 55108, USA
| | - Jeanine M. Refsnider
- Department of Environmental Sciences, University of Toledo, Toledo, OH 43606, USA
| | | | - Daniel A. Warner
- Department of Biological Sciences, Auburn University, Auburn, AL 36849, USA
| | - Fredric J. Janzen
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
- Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060, USA
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21
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Mathewson PD, Darnell MZ, Lane ZM, Yeghissian TG, Levinton J, Porter WP. Incorporating species-specific morphology improves model predictions of thermal and hydric stress in the sand fiddler crab, Leptuca pugilator. J Therm Biol 2023; 115:103613. [PMID: 37437372 DOI: 10.1016/j.jtherbio.2023.103613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 06/03/2023] [Accepted: 06/04/2023] [Indexed: 07/14/2023]
Abstract
Understanding where and why organisms are experiencing thermal and hydric stress is critical for predicting species' responses to climate change. Biophysical models that explicitly link organismal functional traits like morphology, physiology, and behavior to environmental conditions can provide valuable insight into determinants of thermal and hydric stress. Here we use a combination of direct measurements, 3D modeling, and computational fluid dynamics to develop a detailed biophysical model of the sand fiddler crab, Leptuca pugilator. We compare the detailed model's performance to a model using a simpler ellipsoidal approximation of a crab. The detailed model predicted crab body temperatures within 1 °C of observed in both laboratory and field settings; the ellipsoidal approximation model predicted body temperatures within 2 °C of observed body temperatures. Model predictions are meaningfully improved through efforts to incorporate species-specific morphological properties rather than relying on simple geometric approximations. Experimental evaporative water loss (EWL) measurements indicate that L. pugilator can modify its permeability to EWL as a function of vapor density gradients, providing novel insight into physiological thermoregulation in the species. Body temperature and EWL predictions made over the course of a year at a single site demonstrate how such biophysical models can be used to explore mechanistic drivers and spatiotemporal patterns of thermal and hydric stress, providing insight into current and future distributions in the face of climate change.
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Affiliation(s)
- Paul D Mathewson
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI, USA.
| | - M Zachary Darnell
- Division of Coastal Sciences, School of Ocean Science and Engineering, The University of Southern Mississippi, Ocean Springs, MS, USA
| | - Zachary M Lane
- Division of Coastal Sciences, School of Ocean Science and Engineering, The University of Southern Mississippi, Ocean Springs, MS, USA
| | - Talene G Yeghissian
- Division of Coastal Sciences, School of Ocean Science and Engineering, The University of Southern Mississippi, Ocean Springs, MS, USA
| | - Jeffrey Levinton
- Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY, USA
| | - Warren P Porter
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI, USA
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22
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Ledger SEH, Loh J, Almond R, Böhm M, Clements CF, Currie J, Deinet S, Galewski T, Grooten M, Jenkins M, Marconi V, Painter B, Scott-Gatty K, Young L, Hoffmann M, Freeman R, McRae L. Past, present, and future of the Living Planet Index. NPJ BIODIVERSITY 2023; 2:12. [PMID: 39242663 PMCID: PMC11332142 DOI: 10.1038/s44185-023-00017-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 05/05/2023] [Indexed: 09/09/2024]
Abstract
As we enter the next phase of international policy commitments to halt biodiversity loss (e.g., Kunming-Montreal Global Biodiversity Framework), biodiversity indicators will play an important role in forming the robust basis upon which targeted, and time sensitive conservation actions are developed. Population trend indicators are one of the most powerful tools in biodiversity monitoring due to their responsiveness to changes over short timescales and their ability to aggregate species trends from global down to sub-national or even local scale. We consider how the project behind one of the foremost population level indicators - the Living Planet Index - has evolved over the last 25 years, its value to the field of biodiversity monitoring, and how its components have portrayed a compelling account of the changing status of global biodiversity through its application at policy, research and practice levels. We explore ways the project can develop to enhance our understanding of the state of biodiversity and share lessons learned to inform indicator development and mobilise action.
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Affiliation(s)
- Sophie E H Ledger
- Institute of Zoology, Zoological Society of London (ZSL), London, UK.
| | - Jonathan Loh
- School of Anthropology and Conservation, University of Kent, Canterbury, UK
| | - Rosamunde Almond
- WWF Netherlands - World Wide Fund for Nature, Zeist, Netherlands
| | - Monika Böhm
- Global Center for Species Survival, Indianapolis Zoo, Indianapolis, USA
| | | | - Jessica Currie
- WWF Canada - World Wildlife Fund Canada, Toronto, Canada
| | - Stefanie Deinet
- Institute of Zoology, Zoological Society of London (ZSL), London, UK
| | - Thomas Galewski
- Institut de recherche pour la conservation des zones humides méditerranéennes, Tour du Valat, Arles, France
| | - Monique Grooten
- WWF Netherlands - World Wide Fund for Nature, Zeist, Netherlands
| | | | - Valentina Marconi
- Institute of Zoology, Zoological Society of London (ZSL), London, UK
| | - Brett Painter
- Environment and Climate Change Canada (ECCC), Government of Canada, Gatineau, Canada
| | - Kate Scott-Gatty
- Institute of Zoology, Zoological Society of London (ZSL), London, UK
| | - Lucy Young
- WWF UK - World Wide Fund for Nature, Woking, UK
| | - Michael Hoffmann
- Conservation and Policy, Zoological Society of London (ZSL), London, UK
| | - Robin Freeman
- Institute of Zoology, Zoological Society of London (ZSL), London, UK
| | - Louise McRae
- Institute of Zoology, Zoological Society of London (ZSL), London, UK.
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23
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Stewart FEC, Micheletti T, Cumming SG, Barros C, Chubaty AM, Dookie AL, Duclos I, Eddy I, Haché S, Hodson J, Hughes J, Johnson CA, Leblond M, Schmiegelow FKA, Tremblay JA, McIntire EJB. Climate-informed forecasts reveal dramatic local habitat shifts and population uncertainty for northern boreal caribou. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2023; 33:e2816. [PMID: 36752658 DOI: 10.1002/eap.2816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 12/02/2022] [Accepted: 12/14/2022] [Indexed: 06/18/2023]
Abstract
Most research on boreal populations of woodland caribou (Rangifer tarandus caribou) has been conducted in areas of high anthropogenic disturbance. However, a large portion of the species' range overlaps relatively pristine areas primarily affected by natural disturbances, such as wildfire. Climate-driven habitat change is a key concern for the conservation of boreal-dependent species, where management decisions have yet to consider knowledge from multiple ecological domains integrated into a cohesive and spatially explicit forecast of species-specific habitat and demography. We used a novel ecological forecasting framework to provide climate-sensitive projections of habitat and demography for five boreal caribou monitoring areas within the Northwest Territories (NWT), Canada, over 90 years. Importantly, we quantify uncertainty around forecasted mean values. Our results suggest habitat suitability may increase in central and southwest regions of the NWT's Taiga Plains ecozone but decrease in southern and northwestern regions driven by conversion of coniferous to deciduous forests. We do not project that boreal caribou population growth rates will change despite forecasted changes to habitat suitability. Our results emphasize the importance of efforts to protect and restore northern boreal caribou habitat despite climate uncertainty while highlighting expected spatial variations that are important considerations for local people who rely on them. An ability to reproduce previous work, and critical thought when incorporating sources of uncertainty, will be important to refine forecasts, derive management decisions, and improve conservation efficacy for northern species at risk.
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Affiliation(s)
- Frances E C Stewart
- Wilfrid Laurier University, Waterloo, ON, Canada
- Pacific Forestry Centre, Canadian Forest Service, Natural Resources Canada, Victoria, BC, Canada
| | - Tatiane Micheletti
- Department of Forest Resources Management, University of British Columbia, Vancouver, BC, Canada
| | - Steven G Cumming
- Department of Wood and Forest Science, Laval University, Québec, QC, Canada
| | - Ceres Barros
- Department of Forest Resources Management, University of British Columbia, Vancouver, BC, Canada
| | | | | | - Isabelle Duclos
- Environment and Climate Change Canada, Yellowknife, NT, Canada
| | - Ian Eddy
- Pacific Forestry Centre, Canadian Forest Service, Natural Resources Canada, Victoria, BC, Canada
| | - Samuel Haché
- Canadian Wildlife Service, Environment and Climate Change Canada, Yellowknife, NT, Canada
| | - James Hodson
- Department of Environment and Natural Resources, Government of the Northwest Territories, Yellowknife, NT, Canada
| | - Josie Hughes
- Landscape Science and Technology Division, Environment and Climate Change Canada, Ottawa, ON, Canada
| | - Cheryl A Johnson
- Landscape Science and Technology Division, Environment and Climate Change Canada, Ottawa, ON, Canada
| | - Mathieu Leblond
- Landscape Science and Technology Division, Environment and Climate Change Canada, Ottawa, ON, Canada
| | - Fiona K A Schmiegelow
- Department of Renewable Resources, University of Alberta, Edmonton, AB, Canada
- Yukon University, Yukon Research Centre, Whitehorse, YT, Canada
| | - Junior A Tremblay
- Department of Wood and Forest Science, Laval University, Québec, QC, Canada
- Wildlife Research Division, Environment and Climate Change Canada, Québec, QC, Canada
| | - Eliot J B McIntire
- Pacific Forestry Centre, Canadian Forest Service, Natural Resources Canada, Victoria, BC, Canada
- Department of Forest Resources Management, University of British Columbia, Vancouver, BC, Canada
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24
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Blonder BW, Gaüzère P, Iversen LL, Ke P, Petry WK, Ray CA, Salguero‐Gómez R, Sharpless W, Violle C. Predicting and controlling ecological communities via trait and environment mediated parameterizations of dynamical models. OIKOS 2023. [DOI: 10.1111/oik.09415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
Affiliation(s)
- Benjamin Wong Blonder
- Dept of Environmental Science, Policy, and Management, Univ. of California Berkeley CA USA
- School of Life Sciences, Arizona State Univ. Tempe AZ USA
| | - Pierre Gaüzère
- School of Life Sciences, Arizona State Univ. Tempe AZ USA
| | | | - Po‐Ju Ke
- Dept of Ecology & Evolutionary Biology, Princeton Univ. Princeton NJ USA
- Institute of Ecology and Evolutionary Biology, National Taiwan Univ. Taipei Taiwan
| | - William K. Petry
- Dept of Ecology & Evolutionary Biology, Princeton Univ. Princeton NJ USA
- Dept of Plant & Microbial Biology, North Carolina State Univ. Raleigh NC USA
| | - Courtenay A. Ray
- Dept of Environmental Science, Policy, and Management, Univ. of California Berkeley CA USA
- School of Life Sciences, Arizona State Univ. Tempe AZ USA
| | - Roberto Salguero‐Gómez
- Dept of Zoology, Univ. of Oxford Oxford UK
- Max Planck Institute for Demographic Research Rostock Germany
- Center of Excellence in Environmental Decisions, Univ. of Queensland Brisbane Australia
| | - William Sharpless
- Dept of Bioengineering, Univ. of California Berkeley Berkeley CA USA
| | - Cyrille Violle
- CEFE ‐ Univ Montpellier ‐ CNRS – EPHE – IRD Montpellier France
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25
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Rogers TL, Munch SB, Matsuzaki SIS, Symons CC. Intermittent instability is widespread in plankton communities. Ecol Lett 2023; 26:470-481. [PMID: 36707927 DOI: 10.1111/ele.14168] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/02/2023] [Accepted: 01/04/2023] [Indexed: 01/29/2023]
Abstract
Chaotic dynamics appear to be prevalent in short-lived organisms including plankton and may limit long-term predictability. However, few studies have explored how dynamical stability varies through time, across space and at different taxonomic resolutions. Using plankton time series data from 17 lakes and 4 marine sites, we found seasonal patterns of local instability in many species, that short-term predictability was related to local instability, and that local instability occurred most often in the spring, associated with periods of high growth. Taxonomic aggregates were more stable and more predictable than finer groupings. Across sites, higher latitude locations had higher Lyapunov exponents and greater seasonality in local instability, but only at coarser taxonomic resolution. Overall, these results suggest that prediction accuracy, sensitivity to change and management efficacy may be greater at certain times of year and that prediction will be more feasible for taxonomic aggregates.
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Affiliation(s)
- Tanya L Rogers
- Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, California, USA.,Institute of Marine Sciences, University of California, Santa Cruz, California, USA
| | - Stephan B Munch
- Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, California, USA.,Department of Applied Mathematics, University of California, Santa Cruz, California, USA
| | | | - Celia C Symons
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California, USA
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26
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Novel physiological data needed for progress in global change ecology. Basic Appl Ecol 2023. [DOI: 10.1016/j.baae.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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27
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Gallagher BK, Geargeoura S, Fraser DJ. Effects of climate on salmonid productivity: A global meta-analysis across freshwater ecosystems. GLOBAL CHANGE BIOLOGY 2022; 28:7250-7269. [PMID: 36151941 PMCID: PMC9827867 DOI: 10.1111/gcb.16446] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 09/09/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
Salmonids are of immense socio-economic importance in much of the world, but are threatened by climate change. This has generated a substantial literature documenting the effects of climate variation on salmonid productivity in freshwater ecosystems, but there has been no global quantitative synthesis across studies. We conducted a systematic review and meta-analysis to gain quantitative insight into key factors shaping the effects of climate on salmonid productivity, ultimately collecting 1321 correlations from 156 studies, representing 23 species across 24 countries. Fisher's Z was used as the standardized effect size, and a series of weighted mixed-effects models were compared to identify covariates that best explained variation in effects. Patterns in climate effects were complex and were driven by spatial (latitude, elevation), temporal (time-period, age-class), and biological (range, habitat type, anadromy) variation within and among study populations. These trends were often consistent with predictions based on salmonid thermal tolerances. Namely, warming and decreased precipitation tended to reduce productivity when high temperatures challenged upper thermal limits, while opposite patterns were common when cold temperatures limited productivity. Overall, variable climate impacts on salmonids suggest that future declines in some locations may be counterbalanced by gains in others. In particular, we suggest that future warming should (1) increase salmonid productivity at high latitudes and elevations (especially >60° and >1500 m), (2) reduce productivity in populations experiencing hotter and dryer growing season conditions, (3) favor non-native over native salmonids, and (4) impact lentic populations less negatively than lotic ones. These patterns should help conservation and management organizations identify populations most vulnerable to climate change, which can then be prioritized for protective measures. Our framework enables broad inferences about future productivity that can inform decision-making under climate change for salmonids and other taxa, but more widespread, standardized, and hypothesis-driven research is needed to expand current knowledge.
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Affiliation(s)
| | - Sarah Geargeoura
- Department of BiologyConcordia UniversityMontrealQuebecCanada
- Present address:
Environment and Climate Change CanadaGatineauQuebecCanada
| | - Dylan J. Fraser
- Department of BiologyConcordia UniversityMontrealQuebecCanada
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28
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Aldossari S, Husmeier D, Matthiopoulos J. Transferable species distribution modelling: Comparative performance of Generalised Functional Response models. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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29
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Decomposing predictability to identify dominant causal drivers in complex ecosystems. Proc Natl Acad Sci U S A 2022; 119:e2204405119. [PMID: 36215500 PMCID: PMC9586263 DOI: 10.1073/pnas.2204405119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time series-based causal inferences. Here, we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.
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Guillaumot C, Belmaker J, Buba Y, Fourcy D, Dubois P, Danis B, Le Moan E, Saucède T. Classic or hybrid? The performance of next generation ecological models to study the response of Southern Ocean species to changing environmental conditions. DIVERS DISTRIB 2022. [DOI: 10.1111/ddi.13617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Affiliation(s)
- Charlène Guillaumot
- Marine Biology Lab Université Libre de Bruxelles Bruxelles Belgium
- Biogéosciences, UMR 6282 CNRS Université Bourgogne Franche‐Comté Dijon France
| | - Jonathan Belmaker
- School of Zoology, George S. Wise Faculty of Life Sciences Tel Aviv University Tel Aviv Israel
| | - Yehezkel Buba
- School of Zoology, George S. Wise Faculty of Life Sciences Tel Aviv University Tel Aviv Israel
| | - Damien Fourcy
- ESE, Ecology and Ecosystem Health, INRAE Rennes France
| | - Philippe Dubois
- Marine Biology Lab Université Libre de Bruxelles Bruxelles Belgium
| | - Bruno Danis
- Marine Biology Lab Université Libre de Bruxelles Bruxelles Belgium
| | - Eline Le Moan
- Biogéosciences, UMR 6282 CNRS Université Bourgogne Franche‐Comté Dijon France
| | - Thomas Saucède
- Biogéosciences, UMR 6282 CNRS Université Bourgogne Franche‐Comté Dijon France
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31
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Monsalve-Bravo GM, Lawson BAJ, Drovandi C, Burrage K, Brown KS, Baker CM, Vollert SA, Mengersen K, McDonald-Madden E, Adams MP. Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data. SCIENCE ADVANCES 2022; 8:eabm5952. [PMID: 36129974 PMCID: PMC9491719 DOI: 10.1126/sciadv.abm5952] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
This work introduces a comprehensive approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the combination of prior beliefs and data. This approach identifies stiff parameter combinations strongly affecting the quality of the model-data fit while simultaneously revealing which of these key parameter combinations are informed primarily by the data or are also substantively influenced by the priors. We focus on the very common context in complex systems where the amount and quality of data are low compared to the number of model parameters to be collectively estimated, and showcase the benefits of this technique for applications in biochemistry, ecology, and cardiac electrophysiology. We also show how stiff parameter combinations, once identified, uncover controlling mechanisms underlying the system being modeled and inform which of the model parameters need to be prioritized in future experiments for improved parameter inference from collective model-data fitting.
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Affiliation(s)
- Gloria M. Monsalve-Bravo
- School of Earth and Environmental Sciences, The University of Queensland, St Lucia, QLD 4072, Australia
- Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, QLD 4072, Australia
- School of Chemical Engineering, The University of Queensland, St Lucia, QLD 4072, Australia
| | - Brodie A. J. Lawson
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4001, Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, QLD 4001, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Christopher Drovandi
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4001, Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, QLD 4001, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Kevin Burrage
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, QLD 4001, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, Queensland University of Technology, Brisbane, QLD 4001, Australia
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - Kevin S. Brown
- Department of Pharmaceutical Sciences, Oregon State University, Corvallis, OR 97331, USA
- Department of Chemical, Biological, & Environmental Engineering, Oregon State University, Corvallis, OR 97331, USA
| | - Christopher M. Baker
- School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
- Melbourne Centre for Data Science, The University of Melbourne, Parkville, VIC 3010, Australia
- Centre of Excellence for Biosecurity Risk Analysis, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Sarah A. Vollert
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4001, Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, QLD 4001, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Kerrie Mengersen
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4001, Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, QLD 4001, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Eve McDonald-Madden
- School of Earth and Environmental Sciences, The University of Queensland, St Lucia, QLD 4072, Australia
- Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, QLD 4072, Australia
| | - Matthew P. Adams
- School of Chemical Engineering, The University of Queensland, St Lucia, QLD 4072, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4001, Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, QLD 4001, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4001, Australia
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32
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Renault D, Hess MCM, Braschi J, Cuthbert RN, Sperandii MG, Bazzichetto M, Chabrerie O, Thiébaut G, Buisson E, Grandjean F, Bittebiere AK, Mouchet M, Massol F. Advancing biological invasion hypothesis testing using functional diversity indices. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 834:155102. [PMID: 35398434 DOI: 10.1016/j.scitotenv.2022.155102] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/31/2022] [Accepted: 04/03/2022] [Indexed: 06/14/2023]
Abstract
Pioneering investigations on the effects of introduced populations on community structure, ecosystem functioning and services have focused on the effects of invaders on taxonomic diversity. However, taxonomic-based diversity metrics overlook the heterogeneity of species roles within and among communities. As the homogenizing effects of biological invasions on community and ecosystem processes can be subtle, they may require the use of functional diversity indices to be properly evidenced. Starting from the listing of major functional diversity indices, alongside the presentation of their strengths and limitations, we focus on studies pertaining to the effects of invasive species on native communities and recipient ecosystems using functional diversity indices. By doing so, we reveal that functional diversity of the recipient community may strongly vary at the onset of the invasion process, while it stabilizes at intermediate and high levels of invasion. As functional changes occurring during the lag phase of an invasion have been poorly investigated, we show that it is still unknown whether there are consistent changes in functional diversity metrics that could indicate the end of the lag phase. Thus, we recommend providing information on the invasion stage under consideration when computing functional diversity metrics. For the existing literature, it is also surprising that very few studies explored the functional difference between organisms from the recipient communities and invaders of the same trophic levels, or assessed the effects of non-native organism establishment into a non-analogue versus an analogue community. By providing valuable tools for obtaining in-depth diagnostics of community structure and functioning, functional diversity indices can be applied for timely implementation of restoration plans and improved conservation strategies. To conclude, our work provides a first synthetic guide for their use in hypothesis testing in invasion biology.
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Affiliation(s)
- David Renault
- University of Rennes, CNRS, ECOBIO [(Ecosystèmes, biodiversité, évolution)] - UMR 6553, Rennes, France; Institut Universitaire de France, 1 rue Descartes, 75231 Paris Cedex 05, France.
| | - Manon C M Hess
- Institut Méditerranéen de Biodiversité et d'Écologie marine et continentale (IMBE), UMR Aix Marseille Université, Avignon Université, CNRS, IRD, France; Institut de recherche pour la conservation des zones humides méditerranéennes Tour du Valat, Le Sambuc, 13200 Arles, France; NGE-GUINTOLI, Saint-Etienne du Grès, Parc d'activités de Laurade - BP22, 13156 Tarascon Cedex, France
| | - Julie Braschi
- Institut Méditerranéen de Biodiversité et d'Écologie marine et continentale (IMBE), UMR Aix Marseille Université, Avignon Université, CNRS, IRD, France; Naturalia-Environnement, Ingénierie en écologie, 20 Rue Lawrence Durrell, 84140 Avignon, France
| | - Ross N Cuthbert
- GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, 24105 Kiel, Germany; School of Biological Sciences, Queen's University Belfast, BT9 5DL Belfast, United Kingdom
| | - Marta G Sperandii
- Dipartimento di Scienze, Università degli Studi Roma Tre, Viale G. Marconi 446, 00146 Roma, Italy
| | - Manuele Bazzichetto
- University of Rennes, CNRS, ECOBIO [(Ecosystèmes, biodiversité, évolution)] - UMR 6553, Rennes, France
| | - Olivier Chabrerie
- Université de Picardie Jules Verne, UMR 7058 CNRS EDYSAN, 1 rue des Louvels, 80037 Amiens Cedex 1, France
| | - Gabrielle Thiébaut
- University of Rennes, CNRS, ECOBIO [(Ecosystèmes, biodiversité, évolution)] - UMR 6553, Rennes, France
| | - Elise Buisson
- Institut Méditerranéen de Biodiversité et d'Écologie marine et continentale (IMBE), UMR Aix Marseille Université, Avignon Université, CNRS, IRD, France
| | - Frédéric Grandjean
- Université de Poitiers, UMR CNRS 7267 EBI- Ecologie et Biologie des Interactions, équipe EES, 5 rue Albert Turpin, Bat B8-B35, TSA 51106, 86073 Poitiers Cedex 09, France
| | - Anne-Kristel Bittebiere
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR5023 LEHNA, F-69622 Villeurbanne, France
| | - Maud Mouchet
- UMR 7204 MNHN-SU-CNRS CESCO, CP135, 57 rue Cuvier, 75005 Paris, France
| | - François Massol
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019 - UMR 8204 - CIIL - Center for Infection and Immunity of Lille, F-59000 Lille, France
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33
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Alagador D, Cerdeira JO. Operations research applicability in spatial conservation planning. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 315:115172. [PMID: 35525048 DOI: 10.1016/j.jenvman.2022.115172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 04/12/2022] [Accepted: 04/23/2022] [Indexed: 06/14/2023]
Abstract
A large fraction of the current environmental crisis derives from the large rates of human-driven biodiversity loss. Biodiversity conservation questions human practices towards biodiversity and, therefore, largely conflicts with ordinary societal aspirations. Decisions on the location of protected areas, one of the most convincing conservation tools, reflect such a competitive endeavor. Operations Research (OR) brings a set of analytical models and tools capable of resolving the conflicting interests between ecology and economy. Recent technological advances have boosted the size and variety of data available to planners, thus challenging conventional approaches bounded on optimized solutions. New models and methods are needed to use such a massive amount of data in integrative schemes addressing a large variety of concerns. This study provides an overview on the past, present and future challenges that characterize spatial conservation models supported by OR. We discuss the progress of OR models and methods in spatial conservation planning and how those models may be optimized through sophisticated algorithms and computational tools. Moreover, we anticipate possible panoramas of modern spatial conservation studies supported by OR and we explore possible avenues for the design of optimized interdisciplinary collaborative platforms in the era of Big Data, through consortia where distinct players with different motivations and services meet. By enlarging the spatial, temporal, taxonomic and societal horizons of biodiversity conservation, planners navigate around multiple socioecological/environmental equilibria and are able to decide on cost-effective strategies to improve biodiversity persistence under complex environments.
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Affiliation(s)
- Diogo Alagador
- Biodiversity Chair, Institute for Advanced Studies and Research, Universidade de Évora, Rua Joaquim Henrique da Fonseca, Casa Cordovil, 2°, 7000-890, Évora, Portugal; MED - Mediterranean Institute for Agriculture, Environment and Development, CHANGE - Global Change and Sustainability Institute, Universidade de Évora, Évora, Portugal.
| | - Jorge Orestes Cerdeira
- Department of Mathematics, Faculdade de Ciências e Tecnologia da Universidade NOVA de Lisboa, Quinta da Torre, 282 -516, Costa da Caparica, Portugal; Centre for Mathematics and Applications, Faculdade de Ciências e Tecnologia da Universidade NOVA de Lisboa, Quinta da Torre, 282 -516, Costa da Caparica, Portugal.
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34
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Matthiopoulos J. Defining, estimating, and understanding the fundamental niches of complex animals in heterogeneous environments. ECOL MONOGR 2022. [DOI: 10.1002/ecm.1545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Jason Matthiopoulos
- Institute of Biodiversity Animal Health and Comparative Medicine. University of Glasgow. Glasgow. G12 8QQ Scotland
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35
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McIntire EJB, Chubaty AM, Cumming SG, Andison D, Barros C, Boisvenue C, Haché S, Luo Y, Micheletti T, Stewart FEC. PERFICT: A Re-imagined foundation for predictive ecology. Ecol Lett 2022; 25:1345-1351. [PMID: 35315961 PMCID: PMC9310704 DOI: 10.1111/ele.13994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 02/10/2022] [Indexed: 12/02/2022]
Abstract
Making predictions from ecological models-and comparing them to data-offers a coherent approach to evaluate model quality, regardless of model complexity or modelling paradigm. To date, our ability to use predictions for developing, validating, updating, integrating and applying models across scientific disciplines while influencing management decisions, policies, and the public has been hampered by disparate perspectives on prediction and inadequately integrated approaches. We present an updated foundation for Predictive Ecology based on seven principles applied to ecological modelling: make frequent Predictions, Evaluate models, make models Reusable, Freely accessible and Interoperable, built within Continuous workflows that are routinely Tested (PERFICT). We outline some benefits of working with these principles: accelerating science; linking with data science; and improving science-policy integration.
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Affiliation(s)
- Eliot J. B. McIntire
- Pacific Forestry CentreCanadian Forest ServiceNatural Resources CanadaVictoriaBritish ColumbiaCanada
- Faculty of ForestryForest Resources ManagementThe University of British ColumbiaVancouverBritish ColumbiaCanada
- Département des sciences du bois et de la forêtPavillon Abitibi‐Price, 2405, rue de la TerrasseUniversité LavalQuébec CityQuébecCanada
| | - Alex M. Chubaty
- Pacific Forestry CentreCanadian Forest ServiceNatural Resources CanadaVictoriaBritish ColumbiaCanada
- Département des sciences du bois et de la forêtPavillon Abitibi‐Price, 2405, rue de la TerrasseUniversité LavalQuébec CityQuébecCanada
- FOR‐CAST Research & AnalyticsCalgaryAlbertaCanada
| | - Steven G. Cumming
- Département des sciences du bois et de la forêtPavillon Abitibi‐Price, 2405, rue de la TerrasseUniversité LavalQuébec CityQuébecCanada
| | - Dave Andison
- Faculty of ForestryForest Resources ManagementThe University of British ColumbiaVancouverBritish ColumbiaCanada
- Bandaloop Landscape‐Ecosystem Services Ltd.NelsonBritish ColumbiaCanada
| | - Ceres Barros
- Faculty of ForestryForest Resources ManagementThe University of British ColumbiaVancouverBritish ColumbiaCanada
| | - Céline Boisvenue
- Pacific Forestry CentreCanadian Forest ServiceNatural Resources CanadaVictoriaBritish ColumbiaCanada
- Faculty of ForestryForest Resources ManagementThe University of British ColumbiaVancouverBritish ColumbiaCanada
| | - Samuel Haché
- Canadian Wildlife ServiceEnvironment and Climate Change CanadaYellowknifeNorthwest TerritoriesCanada
| | - Yong Luo
- Pacific Forestry CentreCanadian Forest ServiceNatural Resources CanadaVictoriaBritish ColumbiaCanada
- Forest Analysis and Inventory BranchBC Ministry of ForestsVictoriaBritish ColumbiaCanada
| | - Tatiane Micheletti
- Faculty of ForestryForest Resources ManagementThe University of British ColumbiaVancouverBritish ColumbiaCanada
| | - Frances E. C. Stewart
- Pacific Forestry CentreCanadian Forest ServiceNatural Resources CanadaVictoriaBritish ColumbiaCanada
- University of VictoriaSchool of Environmental StudiesVictoriaBritish ColumbiaCanada
- Department of BiologyWilfrid Laurier UniversityWaterlooOntarioCanada
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Ferrín M, Márquez L, Petersen H, Salmon S, Ponge J, Arnedo M, Emmett B, Beier C, Schmidt IK, Tietema A, Angelis P, Liberati D, Kovács‐Láng E, Kröel‐Dulay G, Estiarte M, Bartrons M, Peñuelas J, Peguero G. Trait‐mediated responses to aridity and experimental drought by springtail communities across Europe. Funct Ecol 2022. [DOI: 10.1111/1365-2435.14036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Miquel Ferrín
- CSIC Global Ecology Unit CREAF‐CSIC‐UAB 08913 Bellaterra Catalonia Spain
- CREAF 08913 Cerdanyola del Vallès Catalonia Spain
| | - Laura Márquez
- CSIC Global Ecology Unit CREAF‐CSIC‐UAB 08913 Bellaterra Catalonia Spain
- CREAF 08913 Cerdanyola del Vallès Catalonia Spain
| | - Henning Petersen
- Natural History Museum Mols Laboratory Strandkaervej 6‐8 Femmøller DK8400 Denmark
| | - Sandrine Salmon
- Muséum National d’Histoire Naturelle CNRS UMR 7179 4 Avenue du Petit‐Château 91800 Brunoy France
| | - Jean‐François Ponge
- Muséum National d’Histoire Naturelle CNRS UMR 7179 4 Avenue du Petit‐Château 91800 Brunoy France
| | - Miquel Arnedo
- Department of Evolutionary Biology, Ecology and Environmental Sciences and Biodiversity Research Institute (IRBio) Universitat de Barcelona Avinguda Diagonal 643 08028 Barcelona Spain
| | - Bridget Emmett
- Centre for Ecology and Hydrology Environment Centre Wales, Deiniol Road Bangor LL57 2UW UK
| | - Claus Beier
- Department of Geosciences and Natural Resource Management University of Copenhagen Rolighedsvej 23 1958 Frederiksberg C Denmark
| | - Inger K. Schmidt
- Department of Geosciences and Natural Resource Management University of Copenhagen Rolighedsvej 23 1958 Frederiksberg C Denmark
| | - Albert Tietema
- Institute for Biodiversity and Ecosystem Dynamics University of Amsterdam 94240, 1090 GE Amsterdam The Netherlands
| | - Paolo Angelis
- Department for Innovation in Biological Agro‐food and Forest systems University of Tuscia Via San Camillo de Lellis snc 01100 Viterbo Italy
| | - Dario Liberati
- Department for Innovation in Biological Agro‐food and Forest systems University of Tuscia Via San Camillo de Lellis snc 01100 Viterbo Italy
| | - Edit Kovács‐Láng
- Institute of Ecology and Botany MTA Centre for Ecological Research Alkotmany u. 2‐4 2163 Vacratot Hungary
| | - György Kröel‐Dulay
- Institute of Ecology and Botany MTA Centre for Ecological Research Alkotmany u. 2‐4 2163 Vacratot Hungary
| | - Marc Estiarte
- CSIC Global Ecology Unit CREAF‐CSIC‐UAB 08913 Bellaterra Catalonia Spain
- CREAF 08913 Cerdanyola del Vallès Catalonia Spain
| | - Mireia Bartrons
- Aquatic Ecology Group Universitat de Vic‐ Universitat Central de Catalunya Vic 08500 Barcelona Spain
| | - Josep Peñuelas
- CSIC Global Ecology Unit CREAF‐CSIC‐UAB 08913 Bellaterra Catalonia Spain
- CREAF 08913 Cerdanyola del Vallès Catalonia Spain
| | - Guille Peguero
- CSIC Global Ecology Unit CREAF‐CSIC‐UAB 08913 Bellaterra Catalonia Spain
- CREAF 08913 Cerdanyola del Vallès Catalonia Spain
- Departament de Biologia Animal Biologia Vegetal i Ecologia Universitat Autònoma de Barcelona 08193 Bellaterra Spain
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Tobias JA. A bird in the hand: Global-scale morphological trait datasets open new frontiers of ecology, evolution and ecosystem science. Ecol Lett 2022; 25:573-580. [PMID: 35199920 DOI: 10.1111/ele.13960] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Joseph A Tobias
- Department of Life Sciences, Imperial College London, Ascot, UK
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38
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Schouten R, Baudrot V, Umina P, Maino J. A working guide to spatial mechanistic modelling in Julia. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
| | | | - Paul Umina
- Cesar Australia Parkville Vic Australia
- School of BioSciences The University of Melbourne Parkville Vic Australia
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39
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Neupane N, Zipkin EF, Saunders SP, Ries L. Grappling with uncertainty in ecological projections: a case study using the migratory monarch butterfly. Ecosphere 2022. [DOI: 10.1002/ecs2.3874] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Affiliation(s)
- Naresh Neupane
- Department of Biology Georgetown University Washington D.C. 20057 USA
| | - Elise F. Zipkin
- Department of Integrative Biology Michigan State University East Lansing Michigan 48824 USA
| | | | - Leslie Ries
- Department of Biology Georgetown University Washington D.C. 20057 USA
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40
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Engbersen N, Stefan L, Brooker RW, Schöb C. Using plant traits to understand the contribution of biodiversity effects to annual crop community productivity. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e02479. [PMID: 34657349 PMCID: PMC9286576 DOI: 10.1002/eap.2479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 06/15/2021] [Accepted: 07/08/2021] [Indexed: 06/13/2023]
Abstract
Increasing biodiversity generally enhances productivity through selection and complementarity effects not only in natural, but also in agricultural, systems. However, the quest to explain why diverse cropping systems are more productive than monocultures remains a central goal in agricultural science. In a mesocosm experiment, we constructed monocultures, two- and four-species mixtures from eight crop species with or without fertilizer and both in temperate Switzerland and dry, Mediterranean Spain. We measured physical factors and plant traits and related these in structural equation models to selection and complementarity effects to explain seed yield differences between monocultures and mixtures. Increased crop diversity increased seed yield in Switzerland. This positive biodiversity effect was driven to almost the same extent by selection and complementarity effects, which increased with plant height and specific leaf area (SLA), respectively. Also, ecological processes driving seed yield increases from monocultures to mixtures differed from those responsible for seed yield increases through the diversification of mixtures from two to four species. Whereas selection effects were mainly driven by one species, complementarity effects were linked to larger leaf area per unit leaf weight. Seed yield increases due to mixture diversification were driven only by complementarity effects and were not mediated through the measured traits, suggesting that ecological processes beyond those measured in this study were responsible for positive diversity effects on yield beyond two-species mixtures. By understanding the drivers of positive biodiversity-productivity relationships, we can improve our ability to predict species combinations that enhance ecosystem functioning and can promote sustainable agricultural production.
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Affiliation(s)
- Nadine Engbersen
- Institute of Agricultural SciencesETH Zurich8092ZurichSwitzerland
| | - Laura Stefan
- Institute of Agricultural SciencesETH Zurich8092ZurichSwitzerland
| | | | - Christian Schöb
- Institute of Agricultural SciencesETH Zurich8092ZurichSwitzerland
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41
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Bodner K, Rauen Firkowski C, Bennett JR, Brookson C, Dietze M, Green S, Hughes J, Kerr J, Kunegel‐Lion M, Leroux SJ, McIntire E, Molnár PK, Simpkins C, Tekwa E, Watts A, Fortin M. Bridging the divide between ecological forecasts and environmental decision making. Ecosphere 2021. [DOI: 10.1002/ecs2.3869] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Affiliation(s)
- Korryn Bodner
- Department of Ecology and Evolution University of Toronto Toronto Ontario Canada
- Department of Biological Sciences University of Toronto Scarborough Toronto Ontario Canada
| | - Carina Rauen Firkowski
- Department of Ecology and Evolution University of Toronto Toronto Ontario Canada
- Department of Biology McGill University Montreal Quebec Canada
| | | | - Cole Brookson
- Department of Biological Sciences University of Alberta Edmonton Alberta Canada
| | - Michael Dietze
- Department of Earth & Environment Boston University Boston Massachusetts USA
| | - Stephanie Green
- Department of Biological Sciences University of Alberta Edmonton Alberta Canada
| | - Josie Hughes
- National Wildlife Research Centre Environment and Climate Change Canada Ottawa Ontario Canada
| | - Jeremy Kerr
- Department of Biology University of Ottawa Ottawa Ontario Canada
| | - Mélodie Kunegel‐Lion
- Canadian Forest Service Northern Forestry Centre Natural Resources Canada Edmonton Alberta Canada
| | - Shawn J. Leroux
- Department of Biology Memorial University of Newfoundland St. John’s Newfoundland Canada
| | - Eliot McIntire
- Canadian Forest Service Pacific Forestry Centre Natural Resources Canada Victoria British Columbia Canada
- Faculty of Forestry Forest Resources Management University of British Columbia Vancouver British Columbia Canada
| | - Péter K. Molnár
- Department of Ecology and Evolution University of Toronto Toronto Ontario Canada
- Department of Biological Sciences University of Toronto Scarborough Toronto Ontario Canada
| | - Craig Simpkins
- School of Environment University of Auckland Auckland New Zealand
- Department of Biology Wilfrid Laurier University Waterloo Ontario Canada
- Department of Ecological Modelling Georg‐August University of Goettingen Goettingen Germany
| | - Edward Tekwa
- Department of Zoology University of British Columbia Vancouver British Columbia Canada
| | | | - Marie‐Josée Fortin
- Department of Ecology and Evolution University of Toronto Toronto Ontario Canada
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42
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Uncertainty, Complexity and Constraints: How Do We Robustly Assess Biological Responses under a Rapidly Changing Climate? CLIMATE 2021. [DOI: 10.3390/cli9120177] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
How robust is our assessment of impacts to ecosystems and species from a rapidly changing climate during the 21st century? We examine the challenges of uncertainty, complexity and constraints associated with applying climate projections to understanding future biological responses. This includes an evaluation of how to incorporate the uncertainty associated with different greenhouse gas emissions scenarios and climate models, and constraints of spatiotemporal scales and resolution of climate data into impact assessments. We describe the challenges of identifying relevant climate metrics for biological impact assessments and evaluate the usefulness and limitations of different methodologies of applying climate change to both quantitative and qualitative assessments. We discuss the importance of incorporating extreme climate events and their stochastic tendencies in assessing ecological impacts and transformation, and provide recommendations for better integration of complex climate–ecological interactions at relevant spatiotemporal scales. We further recognize the compounding nature of uncertainty when accounting for our limited understanding of the interactions between climate and biological processes. Given the inherent complexity in ecological processes and their interactions with climate, we recommend integrating quantitative modeling with expert elicitation from diverse disciplines and experiential understanding of recent climate-driven ecological processes to develop a more robust understanding of ecological responses under different scenarios of future climate change. Inherently complex interactions between climate and biological systems also provide an opportunity to develop wide-ranging strategies that resource managers can employ to prepare for the future.
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43
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Albery GF, Becker DJ, Brierley L, Brook CE, Christofferson RC, Cohen LE, Dallas TA, Eskew EA, Fagre A, Farrell MJ, Glennon E, Guth S, Joseph MB, Mollentze N, Neely BA, Poisot T, Rasmussen AL, Ryan SJ, Seifert S, Sjodin AR, Sorrell EM, Carlson CJ. The science of the host-virus network. Nat Microbiol 2021; 6:1483-1492. [PMID: 34819645 DOI: 10.1038/s41564-021-00999-5] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 10/18/2021] [Indexed: 01/21/2023]
Abstract
Better methods to predict and prevent the emergence of zoonotic viruses could support future efforts to reduce the risk of epidemics. We propose a network science framework for understanding and predicting human and animal susceptibility to viral infections. Related approaches have so far helped to identify basic biological rules that govern cross-species transmission and structure the global virome. We highlight ways to make modelling both accurate and actionable, and discuss the barriers that prevent researchers from translating viral ecology into public health policies that could prevent future pandemics.
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Affiliation(s)
- Gregory F Albery
- Department of Biology, Georgetown University, Washington DC, USA.
| | - Daniel J Becker
- Department of Biology, University of Oklahoma, Norman, OK, USA
| | - Liam Brierley
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Cara E Brook
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA
| | | | - Lily E Cohen
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tad A Dallas
- Department of Biological Sciences, University of South Carolina, Columbia, SC, USA
| | - Evan A Eskew
- Department of Biology, Pacific Lutheran University, Tacoma, WA, USA
| | - Anna Fagre
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, USA
| | - Maxwell J Farrell
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Emma Glennon
- Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Sarah Guth
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Maxwell B Joseph
- Earth Lab, Cooperative Institute for Research in Environmental Science, University of Colorado Boulder, Boulder, CO, USA
| | - Nardus Mollentze
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK.,MRC - University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Benjamin A Neely
- National Institute of Standards and Technology, Charleston, SC, USA
| | - Timothée Poisot
- Québec Centre for Biodiversity Sciences, Montréal, Québec, Canada.,Département de Sciences Biologiques, Université de Montréal, Montréal, Québec, Canada
| | - Angela L Rasmussen
- Vaccine and Infectious Disease Organization, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.,Department of Biochemistry, Microbiology, and Immunology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Sadie J Ryan
- Department of Geography, University of Florida, Gainesville, FL, USA.,Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.,School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Stephanie Seifert
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA, USA
| | - Anna R Sjodin
- Department of Biological Sciences, University of Idaho, Moscow, ID, USA
| | - Erin M Sorrell
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC, USA.,Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC, USA
| | - Colin J Carlson
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC, USA. .,Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC, USA.
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44
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Bryn A, Bekkby T, Rinde E, Gundersen H, Halvorsen R. Reliability in Distribution Modeling—A Synthesis and Step-by-Step Guidelines for Improved Practice. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.658713] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Information about the distribution of a study object (e.g., species or habitat) is essential in face of increasing pressure from land or sea use, and climate change. Distribution models are instrumental for acquiring such information, but also encumbered by uncertainties caused by different sources of error, bias and inaccuracy that need to be dealt with. In this paper we identify the most common sources of uncertainties and link them to different phases in the modeling process. Our aim is to outline the implications of these uncertainties for the reliability of distribution models and to summarize the precautions needed to be taken. We performed a step-by-step assessment of errors, biases and inaccuracies related to the five main steps in a standard distribution modeling process: (1) ecological understanding, assumptions and problem formulation; (2) data collection and preparation; (3) choice of modeling method, model tuning and parameterization; (4) evaluation of models; and, finally, (5) implementation and use. Our synthesis highlights the need to consider the entire distribution modeling process when the reliability and applicability of the models are assessed. A key recommendation is to evaluate the model properly by use of a dataset that is collected independently of the training data. We support initiatives to establish international protocols and open geodatabases for distribution models.
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45
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46
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Bradter U, Ozgul A, Griesser M, Layton‐Matthews K, Eggers J, Singer A, Sandercock BK, Haverkamp PJ, Snäll T. Habitat suitability models based on opportunistic citizen science data: Evaluating forecasts from alternative methods versus an individual‐based model. DIVERS DISTRIB 2021. [DOI: 10.1111/ddi.13409] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Affiliation(s)
- Ute Bradter
- SLU Swedish Species Information Centre Swedish University of Agricultural Sciences Uppsala Sweden
- Department of Terrestrial Ecology Norwegian Institute for Nature Research Trondheim Norway
| | - Arpat Ozgul
- Department of Evolutionary Biology and Environmental Studies University of Zurich Zurich Switzerland
| | - Michael Griesser
- Department of Evolutionary Biology and Environmental Studies University of Zurich Zurich Switzerland
- Department of Biology University of Konstanz Konstanz Germany
- Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany
| | - Kate Layton‐Matthews
- Department of Evolutionary Biology and Environmental Studies University of Zurich Zurich Switzerland
- Norwegian Institute for Nature Research Tromsø Norway
| | - Jeannette Eggers
- SLU Swedish Species Information Centre Swedish University of Agricultural Sciences Uppsala Sweden
- Department of Forest Resource Management Swedish University of Agricultural Sciences Umeå Sweden
| | - Alexander Singer
- SLU Swedish Species Information Centre Swedish University of Agricultural Sciences Uppsala Sweden
| | - Brett K. Sandercock
- Department of Terrestrial Ecology Norwegian Institute for Nature Research Trondheim Norway
| | - Paul J. Haverkamp
- Department of Evolutionary Biology and Environmental Studies University of Zurich Zurich Switzerland
| | - Tord Snäll
- SLU Swedish Species Information Centre Swedish University of Agricultural Sciences Uppsala Sweden
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47
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Scavia D, Bertani I, Testa JM, Bever AJ, Blomquist JD, Friedrichs MAM, Linker LC, Michael BD, Murphy RR, Shenk GW. Advancing estuarine ecological forecasts: seasonal hypoxia in Chesapeake Bay. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02384. [PMID: 34128283 PMCID: PMC8459276 DOI: 10.1002/eap.2384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 04/28/2021] [Accepted: 05/26/2021] [Indexed: 06/12/2023]
Abstract
Ecological forecasts are quantitative tools that can guide ecosystem management. The coemergence of extensive environmental monitoring and quantitative frameworks allows for widespread development and continued improvement of ecological forecasting systems. We use a relatively simple estuarine hypoxia model to demonstrate advances in addressing some of the most critical challenges and opportunities of contemporary ecological forecasting, including predictive accuracy, uncertainty characterization, and management relevance. We explore the impacts of different combinations of forecast metrics, drivers, and driver time windows on predictive performance. We also incorporate multiple sets of state-variable observations from different sources and separately quantify model prediction error and measurement uncertainty through a flexible Bayesian hierarchical framework. Results illustrate the benefits of (1) adopting forecast metrics and drivers that strike an optimal balance between predictability and relevance to management, (2) incorporating multiple data sources in the calibration data set to separate and propagate different sources of uncertainty, and (3) using the model in scenario mode to probabilistically evaluate the effects of alternative management decisions on future ecosystem state. In the Chesapeake Bay, the subject of this case study, we find that average summer or total annual hypoxia metrics are more predictable than monthly metrics and that measurement error represents an important source of uncertainty. Application of the model in scenario mode suggests that absent watershed management actions over the past decades, long-term average hypoxia would have increased by 7% compared to 1985. Conversely, the model projects that if management goals currently in place to restore the Bay are met, long-term average hypoxia would eventually decrease by 32% with respect to the mid-1980s.
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Affiliation(s)
- Donald Scavia
- School for Environment and SustainabilityUniversity of MichiganAnn ArborMichigan48103USA
| | - Isabella Bertani
- Chesapeake Bay Program OfficeUniversity of Maryland Center for Environmental ScienceAnnapolisMaryland21403USA
| | - Jeremy M. Testa
- Chesapeake Biological LaboratoryUniversity of Maryland Center for Environmental ScienceSolomonsMaryland20688USA
| | | | - Joel D. Blomquist
- U.S. Geological Survey, Water Observing Systems ProgramBaltimoreMaryland21228USA
| | | | - Lewis C. Linker
- U.S. EPA Chesapeake Bay Program OfficeAnnapolisMaryland21403USA
| | | | - Rebecca R. Murphy
- Chesapeake Bay Program OfficeUniversity of Maryland Center for Environmental ScienceAnnapolisMaryland21403USA
| | - Gary W. Shenk
- U.S. Geological Survey Chesapeake Bay Program OfficeAnnapolisMaryland21403USA
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48
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Orr JA, Piggott JJ, Jackson AL, Arnoldi J. Scaling up uncertain predictions to higher levels of organisation tends to underestimate change. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13621] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- James A. Orr
- Zoology Department School of Natural Sciences Trinity College Dublin Dublin Ireland
| | - Jeremy J. Piggott
- Zoology Department School of Natural Sciences Trinity College Dublin Dublin Ireland
| | - Andrew L. Jackson
- Zoology Department School of Natural Sciences Trinity College Dublin Dublin Ireland
| | - Jean‐François Arnoldi
- Zoology Department School of Natural Sciences Trinity College Dublin Dublin Ireland
- Theoretical and Experimental Ecology Station CNRS Moulis Moulis France
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49
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Schlaepfer DR, Bradford JB, Lauenroth WK, Shriver RK. Understanding the future of big sagebrush regeneration: challenges of projecting complex ecological processes. Ecosphere 2021. [DOI: 10.1002/ecs2.3695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
- Daniel R. Schlaepfer
- Southwest Biological Science Center U.S. Geological Survey Flagstaff Arizona 86001 USA
- Center for Adaptable Western Landscapes Northern Arizona University Flagstaff Arizona 86011 USA
- Yale School of the Environment Yale University New Haven Connecticut 06511 USA
| | - John B. Bradford
- Southwest Biological Science Center U.S. Geological Survey Flagstaff Arizona 86001 USA
| | - William K. Lauenroth
- Yale School of the Environment Yale University New Haven Connecticut 06511 USA
- Department of Botany University of Wyoming Laramie Wyoming 82071 USA
| | - Robert K. Shriver
- Department of Natural Resources and Environmental Science University of Nevada‐Reno Reno Nevada 89557 USA
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50
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Pardal A, Cordeiro CAMM, Ciotti ÁM, Jenkins SR, Giménez L, Burrows MT, Christofoletti RA. Influence of environmental variables over multiple spatial scales on the population structure of a key marine invertebrate. MARINE ENVIRONMENTAL RESEARCH 2021; 170:105410. [PMID: 34271484 DOI: 10.1016/j.marenvres.2021.105410] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 07/02/2021] [Accepted: 07/07/2021] [Indexed: 06/13/2023]
Abstract
Quantifying scale-dependent patterns and linking ecological to environmental variation is required to understand mechanisms regulating biodiversity. We conducted a large-scale survey in rocky shores along the SE Brazilian coast to examine spatial variability in body size and density of an intertidal barnacle (Chthamalus bisinuatus) and its relationships with benthic and oceanographic predictors. Both the size and density of barnacles showed most variation at the smallest spatial scales. On average, barnacle body size was larger on shores located in areas characterised by higher chlorophyll levels, colder waters, low wave action and low influence of freshwater. Barnacles were more abundant at wave-exposed shores. We identified critical scales of spatial variation of an important species and linked population patterns to essential environmental predictors. Our results show that populations of this barnacle are coupled to scale-dependent oceanographic variation. This study offers insights into the mechanisms regulating coastal populations along a little studied coastline.
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Affiliation(s)
- André Pardal
- Center of Natural and Human Sciences, Federal University of ABC (CCNH/UFABC), Rua Santa Adélia, 166, Santo André, SP, 09210-170, Brazil; Institute of Marine Science, Federal University of São Paulo (IMar/UNIFESP), Rua Dr Carvalho de Mendonça 144, Santos, SP, 11070-100, Brazil.
| | - César A M M Cordeiro
- Institute of Marine Science, Federal University of São Paulo (IMar/UNIFESP), Rua Dr Carvalho de Mendonça 144, Santos, SP, 11070-100, Brazil; Marine Biology Department, Federal Fluminense University (LECAR/UFF), Outeiro de São João Batista, s/n, Niterói, RJ, 24020-141, Brazil
| | - Áurea M Ciotti
- Center for Marine Biology, University of São Paulo (CEBIMar/USP), Rod. Manoel Hipólito do Rego, km 131.5, São Sebastião, SP, 1160-000, Brazil
| | - Stuart R Jenkins
- School of Ocean Sciences, Bangor University, Menai Bridge, Anglesey, LL59 5AB, UK
| | - Luis Giménez
- School of Ocean Sciences, Bangor University, Menai Bridge, Anglesey, LL59 5AB, UK
| | - Michael T Burrows
- Scottish Association for Marine Science, Scottish Marine Institute, Oban, Argyll, PA37 1QA, UK
| | - Ronaldo A Christofoletti
- Institute of Marine Science, Federal University of São Paulo (IMar/UNIFESP), Rua Dr Carvalho de Mendonça 144, Santos, SP, 11070-100, Brazil
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