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Braun CD, Arostegui MC, Farchadi N, Alexander M, Afonso P, Allyn A, Bograd SJ, Brodie S, Crear DP, Culhane EF, Curtis TH, Hazen EL, Kerney A, Lezama-Ochoa N, Mills KE, Pugh D, Queiroz N, Scott JD, Skomal GB, Sims DW, Thorrold SR, Welch H, Young-Morse R, Lewison RL. Building use-inspired species distribution models: Using multiple data types to examine and improve model performance. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2023; 33:e2893. [PMID: 37285072 DOI: 10.1002/eap.2893] [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: 02/01/2023] [Accepted: 05/22/2023] [Indexed: 06/08/2023]
Abstract
Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery dependent (conventional mark-recapture tags, fisheries observer records) and two fishery independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage the strengths of individual data types while statistically accounting for limitations, such as sampling biases.
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Affiliation(s)
- Camrin D Braun
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
| | - Martin C Arostegui
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
| | - Nima Farchadi
- Institute for Ecological Monitoring and Management, San Diego State University, San Diego, California, USA
| | | | - Pedro Afonso
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
- Okeanos and Institute of Marine Research, University of the Azores, Horta, Portugal
| | - Andrew Allyn
- Gulf of Maine Research Institute, Portland, Maine, USA
| | - Steven J Bograd
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Monterey, California, USA
| | - Stephanie Brodie
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Monterey, California, USA
- Institute of Marine Sciences, University of California, Santa Cruz, California, USA
| | - Daniel P Crear
- ECS Federal, in Support of National Marine Fisheries Service, Atlantic Highly Migratory Species Management Division, Silver Spring, Maryland, USA
| | - Emmett F Culhane
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
- Massachusetts Institute of Technology-Woods Hole Oceanographic Institution Joint Program in Oceanography-Applied Ocean Science and Engineering, Cambridge, Massachusetts, USA
| | - Tobey H Curtis
- National Marine Fisheries Service, Atlantic Highly Migratory Species Management Division, Gloucester, Massachusetts, USA
| | - Elliott L Hazen
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Monterey, California, USA
- Institute of Marine Sciences, University of California, Santa Cruz, California, USA
| | - Alex Kerney
- Gulf of Maine Research Institute, Portland, Maine, USA
| | - Nerea Lezama-Ochoa
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Monterey, California, USA
- Institute of Marine Sciences, University of California, Santa Cruz, California, USA
| | | | - Dylan Pugh
- Gulf of Maine Research Institute, Portland, Maine, USA
| | - Nuno Queiroz
- Research Network in Biodiversity and Evolutionary Biology, Universidade do Porto, Vairão, Portugal
- Marine Biological Association of the United Kingdom, The Laboratory, Plymouth, UK
| | - James D Scott
- NOAA Earth System Research Laboratory, Boulder, Colorado, USA
- Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, USA
| | - Gregory B Skomal
- Massachusetts Division of Marine Fisheries, New Bedford, Massachusetts, USA
| | - David W Sims
- Marine Biological Association of the United Kingdom, The Laboratory, Plymouth, UK
- Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Southampton, UK
| | - Simon R Thorrold
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
| | - Heather Welch
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Monterey, California, USA
- Institute of Marine Sciences, University of California, Santa Cruz, California, USA
| | | | - Rebecca L Lewison
- Institute for Ecological Monitoring and Management, San Diego State University, San Diego, California, USA
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Boussarie G, Kopp D, Lavialle G, Mouchet M, Morfin M. Marine spatial planning to solve increasing conflicts at sea: A framework for prioritizing offshore windfarms and marine protected areas. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 339:117857. [PMID: 37031598 DOI: 10.1016/j.jenvman.2023.117857] [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: 12/16/2022] [Revised: 03/10/2023] [Accepted: 03/31/2023] [Indexed: 05/03/2023]
Abstract
Direct and indirect anthropogenic pressures on biodiversity and ecosystems are expected to lower the provided ecosystem services (ES) in the near future. To limit these impacts, protected areas will be implemented as part of the Post-2020 Global Biodiversity Framework. Simultaneously, as an answer to climate change, renewable energies are being rapidly developed on a worldwide scale, leading to a significant increase in space use in the coming decades. Sharing space is an increasingly complex task, especially because of the high rate of emergence of such competitors for space. In fisheries-dominated socio-ecosystems, acceptability of offshore windfarms (OWFs) and marine protected areas (MPAs) is usually very low, partly due to an underrepresentation of fisheries in spatial plans and poor attention to equity in the spatial distribution of restrictive areas. Here we developed a framework with a marine spatial planning case study in the Bay of Biscay represented by the socio-ecosystem of the Grande Vasière, a mid-shelf mud belt spanning over 21,000 km2. We collected biological, environmental, and anthropogenic data to model the distribution of 62 bentho-demersal species, 7 regulating ES layers related to nutrient cycling, life cycle maintenance and food web functioning, as well as provisioning ES of 18 commercial species and 82 fisheries subdivisions. We used these spatial layers and a prioritization algorithm to explore siting scenarios of OWFs and two types of MPAs (benthic and total protection), aimed at conserving species, regulating and provisioning ES, while also ensuring that fisheries are equitably impacted. We demonstrate that equitable scenarios are not necessarily costlier and provide alternative spatial prioritizations. We emphasize the importance of exploring multiple targets with a Shiny app to visualize results and stimulate dialogue among stakeholders and policymakers. Overall, we show how our flexible, inclusive framework with particular attention to equity could be an ideal discussion tool to improve management practices.
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Affiliation(s)
- Germain Boussarie
- UMR MNHN-SU-CNRS 7204 CESCO, 43 rue Buffon, CP 135, 75005 Paris, France.
| | - Dorothée Kopp
- UMR IFREMER-INRAE-Institut Agro DECOD, 8 rue François Toullec, CS60012, 56325 Lorient Cedex, France
| | - Gaël Lavialle
- UMR MNHN-SU-CNRS 7204 CESCO, 43 rue Buffon, CP 135, 75005 Paris, France
| | - Maud Mouchet
- UMR MNHN-SU-CNRS 7204 CESCO, 43 rue Buffon, CP 135, 75005 Paris, France
| | - Marie Morfin
- UMR IFREMER-INRAE-Institut Agro DECOD, 8 rue François Toullec, CS60012, 56325 Lorient Cedex, France
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Lippert F, Kranstauber B, Forré PD, van Loon EE. Learning to predict spatiotemporal movement dynamics from weather radar networks. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.14007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Fiona Lippert
- AI4Science Lab University of Amsterdam Amsterdam The Netherlands
- Amsterdam Machine Learning Lab University of Amsterdam Amsterdam The Netherlands
- Institute for Biodiversity and Ecosystem Dynamics University of Amsterdam Amsterdam The Netherlands
| | - Bart Kranstauber
- Institute for Biodiversity and Ecosystem Dynamics University of Amsterdam Amsterdam The Netherlands
| | - Patrick D. Forré
- AI4Science Lab University of Amsterdam Amsterdam The Netherlands
- Amsterdam Machine Learning Lab University of Amsterdam Amsterdam The Netherlands
| | - E. Emiel van Loon
- Institute for Biodiversity and Ecosystem Dynamics University of Amsterdam Amsterdam The Netherlands
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Denoël M, Duret C, Lorrain-Soligon L, Padilla P, Pavis J, Pille F, Tendron P, Ficetola GF, Falaschi M. High habitat invasibility unveils the invasiveness potential of water frogs. Biol Invasions 2022. [DOI: 10.1007/s10530-022-02849-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Frans VF, Augé AA, Fyfe J, Zhang Y, McNally N, Edelhoff H, Balkenhol N, Engler JO. Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Veronica F. Frans
- Center for Systems Integration and Sustainability Department of Fisheries and Wildlife Michigan State University East Lansing MI USA
- Ecology, Evolution, and Behavior Program Michigan State University East Lansing MI USA
- Wildlife Sciences University of Göttingen Göttingen Germany
| | | | - Jim Fyfe
- Department of Conservation Ōtepoti/Dunedin Office Dunedin New Zealand
| | - Yuqian Zhang
- Center for Systems Integration and Sustainability Department of Fisheries and Wildlife Michigan State University East Lansing MI USA
| | | | - Hendrik Edelhoff
- Wildlife Sciences University of Göttingen Göttingen Germany
- Bavarian State Institute of Forestry Freising Germany
| | - Niko Balkenhol
- Wildlife Sciences University of Göttingen Göttingen Germany
| | - Jan O. Engler
- Terrestrial Ecology Unit Ghent University Ghent Belgium
- Chair of Computational Landscape Ecology Technische Universität Dresden Dresden Germany
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Bioacoustics for in situ validation of species distribution modelling: An example with bats in Brazil. PLoS One 2021; 16:e0248797. [PMID: 34669707 PMCID: PMC8528307 DOI: 10.1371/journal.pone.0248797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 10/06/2021] [Indexed: 11/29/2022] Open
Abstract
Species distribution modelling (SDM) gained importance on biodiversity distribution and conservation studies worldwide, including prioritizing areas for public policies and international treaties. Useful for large-scale approaches and species distribution estimates, it is a plus considering that a minor fraction of the planet is adequately sampled. However, minimizing errors is challenging, but essential, considering the uses and consequences of such models. In situ validation of the SDM outputs should be a key-step—in some cases, urgent. Bioacoustics can be used to validate and refine those outputs, especially if the focal species’ vocalizations are conspicuous and species-specific. This is the case of echolocating bats. Here, we used extensive acoustic monitoring (>120 validation points over an area of >758,000 km2, and producing >300,000 sound files) to validate MaxEnt outputs for six neotropical bat species in a poorly-sampled region of Brazil. Based on in situ validation, we evaluated four threshold-dependent theoretical evaluation metrics’ ability in predicting models’ performance. We also assessed the performance of three widely used thresholds to convert continuous SDMs into presence/absence maps. We demonstrated that MaxEnt produces very different outputs, requiring a careful choice on thresholds and modeling parameters. Although all theoretical evaluation metrics studied were positively correlated with accuracy, we empirically demonstrated that metrics based on specificity-sensitivity and sensitivity-precision are better for testing models, considering that most SDMs are based on unbalanced data. Without independent field validation, we found that using an arbitrary threshold for modelling can be a precarious approach with many possible outcomes, even after getting good evaluation scores. Bioacoustics proved to be important for validating SDMs for the six bat species analyzed, allowing a better refinement of SDMs in large and under-sampled regions, with relatively low sampling effort. Regardless of the species assessing method used, our research highlighted the vital necessity of in situ validation for SDMs.
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Floury M, Pollock LJ, Buisson L, Thuiller W, Chandesris A, Souchon Y. Combining expert‐based and computational approaches to design protected river networks under climate change. DIVERS DISTRIB 2021. [DOI: 10.1111/ddi.13411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Affiliation(s)
- Mathieu Floury
- RiverLY Research Unit National Research Institute for Agriculture, Food and Environment (INRAE) Villeurbanne France
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR 5023 LEHNA Villeurbanne F‐69622 France
| | - Laura J. Pollock
- Department of Biology McGill University, 1205 Dr. Penfield Montreal Québec H3A 1B1 Canada
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, Laboratoire d’Écologie Alpine, F‐38000 Grenoble France
| | - Laëtitia Buisson
- Laboratoire écologie fonctionnelle et environnement Université de Toulouse, CNRS, Toulouse INP, Université Toulouse 3 ‐ Paul Sabatier (UPS) Toulouse France
| | - Wilfried Thuiller
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, Laboratoire d’Écologie Alpine, F‐38000 Grenoble France
| | - André Chandesris
- RiverLY Research Unit National Research Institute for Agriculture, Food and Environment (INRAE) Villeurbanne France
| | - Yves Souchon
- RiverLY Research Unit National Research Institute for Agriculture, Food and Environment (INRAE) Villeurbanne France
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Timoner P, Fasel M, Ashraf Vaghefi SS, Marle P, Castella E, Moser F, Lehmann A. Impacts of climate change on aquatic insects in temperate alpine regions: Complementary modeling approaches applied to Swiss rivers. GLOBAL CHANGE BIOLOGY 2021; 27:3565-3581. [PMID: 33837599 PMCID: PMC8360013 DOI: 10.1111/gcb.15637] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 03/08/2021] [Accepted: 03/28/2021] [Indexed: 06/12/2023]
Abstract
Freshwater biodiversity loss is a major concern, and global warming is already playing a significant role in species extinctions. Our main goal was to predict climate change impacts on aquatic insect species distribution and richness in Swiss running waters according to two climate change scenarios (RCP2.6 and RCP8.5), using different modeling approaches, that is, species distribution models (SDMs), stacked-SDMs (S-SDMs) and a macroecological model (MEM). We analyzed 10,808 reaches, used as spatial units for model predictions, for a total river network length of 20,610 km. Results were assessed at both the countrywide and the biogeographic regional scales. We used incidence data of 41 species of Ephemeroptera, Plecoptera and Trichoptera (EPT) from 259 sites distributed across Switzerland. We integrated a coupled model for hydrology and glacier retreat to simulate monthly time-step discharge from which we derived hydrological variables. These, along with thermal, land-cover, topographic and spatially explicit data, served as predictors for our ecological models. Predictions of occurrence probabilities and EPT richness were compared among the different regions, periods and scenarios. A Shiny web application was developed to interactively explore all the models' details, to ensure transparency and promote the sharing of information. MEM and S-SDMs approaches consistently showed that overall, climate change is likely to reduce EPT richness. Decrease could be around 10% in the least conservative scenario, depending on the region. Global warming was shown to represent a threat to species from high elevation, but in terms of species richness, running waters from lowlands and medium elevation seemed more vulnerable. Finally, our results suggested that the effects of anthropogenic activities could overweight natural factors in shaping the future of river biodiversity.
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Affiliation(s)
- Pablo Timoner
- enviroSPACE GroupDepartment F.‐A. Forel for Environmental and Aquatic SciencesUniversity of GenevaInstitute for Environmental SciencesGenevaSwitzerland
| | - Marc Fasel
- enviroSPACE GroupDepartment F.‐A. Forel for Environmental and Aquatic SciencesUniversity of GenevaInstitute for Environmental SciencesGenevaSwitzerland
| | | | - Pierre Marle
- Aquatic Ecology GroupDepartment F.‐A. Forel for Environmental and Aquatic SciencesUniversity of GenevaInstitute for Environmental SciencesGenevaSwitzerland
| | - Emmanuel Castella
- Aquatic Ecology GroupDepartment F.‐A. Forel for Environmental and Aquatic SciencesUniversity of GenevaInstitute for Environmental SciencesGenevaSwitzerland
| | - Frédéric Moser
- GRID‐GenevaUniversity of GenevaInstitute for Environmental SciencesGenevaSwitzerland
| | - Anthony Lehmann
- enviroSPACE GroupDepartment F.‐A. Forel for Environmental and Aquatic SciencesUniversity of GenevaInstitute for Environmental SciencesGenevaSwitzerland
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Burian A, Mauvisseau Q, Bulling M, Domisch S, Qian S, Sweet M. Improving the reliability of eDNA data interpretation. Mol Ecol Resour 2021; 21:1422-1433. [PMID: 33655639 DOI: 10.1111/1755-0998.13367] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 01/07/2021] [Accepted: 02/24/2021] [Indexed: 02/06/2023]
Abstract
Global declines in biodiversity highlight the need to effectively monitor the density and distribution of threatened species. In recent years, molecular survey methods detecting DNA released by target-species into their environment (eDNA) have been rapidly on the rise. Despite providing new, cost-effective tools for conservation, eDNA-based methods are prone to errors. Best field and laboratory practices can mitigate some, but the risks of errors cannot be eliminated and need to be accounted for. Here, we synthesize recent advances in data processing tools that increase the reliability of interpretations drawn from eDNA data. We review advances in occupancy models to consider spatial data-structures and simultaneously assess rates of false positive and negative results. Further, we introduce process-based models and the integration of metabarcoding data as complementing approaches to increase the reliability of target-species assessments. These tools will be most effective when capitalizing on multi-source data sets collating eDNA with classical survey and citizen-science approaches, paving the way for more robust decision-making processes in conservation planning.
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Affiliation(s)
- Alfred Burian
- Aquatic Research Facility, Environmental Sustainability Research Centre, University of Derby, Derby, UK.,Marine Ecology Department, Lurio University, Nampula, Mozambique.,Department of Computational Landscape Ecology, UFZ - Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Quentin Mauvisseau
- Aquatic Research Facility, Environmental Sustainability Research Centre, University of Derby, Derby, UK.,Natural History Museum, University of Oslo, Oslo, Norway
| | - Mark Bulling
- Aquatic Research Facility, Environmental Sustainability Research Centre, University of Derby, Derby, UK
| | - Sami Domisch
- Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
| | - Song Qian
- Department of Environmental Sciences, University of Toledo, Toledo, OH, USA
| | - Michael Sweet
- Aquatic Research Facility, Environmental Sustainability Research Centre, University of Derby, Derby, UK
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Continent-Wide Tree Species Distribution Models May Mislead Regional Management Decisions: A Case Study in the Transboundary Biosphere Reserve Mura-Drava-Danube. FORESTS 2021. [DOI: 10.3390/f12030330] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
The understanding of spatial distribution patterns of native riparian tree species in Europe lacks accurate species distribution models (SDMs), since riparian forest habitats have a limited spatial extent and are strongly related to the associated watercourses, which needs to be represented in the environmental predictors. However, SDMs are urgently needed for adapting forest management to climate change, as well as for conservation and restoration of riparian forest ecosystems. For such an operative use, standard large-scale bioclimatic models alone are too coarse and frequently exclude relevant predictors. In this study, we compare a bioclimatic continent-wide model and a regional model based on climate, soil, and river data for central to south-eastern Europe, targeting seven riparian foundation species—Alnus glutinosa, Fraxinus angustifolia, F. excelsior, Populus nigra, Quercus robur, Ulmus laevis, and U. minor. The results emphasize the high importance of precise occurrence data and environmental predictors. Soil predictors were more important than bioclimatic variables, and river variables were partly of the same importance. In both models, five of the seven species were found to decrease in terms of future occurrence probability within the study area, whereas the results for two species were ambiguous. Nevertheless, both models predicted a dangerous loss of occurrence probability for economically and ecologically important tree species, likely leading to significant effects on forest composition and structure, as well as on provided ecosystem services.
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Friedrichs‐Manthey M, Langhans SD, Hein T, Borgwardt F, Kling H, Jähnig SC, Domisch S. From topography to hydrology-The modifiable area unit problem impacts freshwater species distribution models. Ecol Evol 2020; 10:2956-2968. [PMID: 32211168 PMCID: PMC7083667 DOI: 10.1002/ece3.6110] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 01/12/2020] [Indexed: 11/06/2022] Open
Abstract
Species distribution models (SDMs) are statistical tools to identify potentially suitable habitats for species. For SDMs in river ecosystems, species occurrences and predictor data are often aggregated across subcatchments that serve as modeling units. The level of aggregation (i.e., model resolution) influences the statistical relationships between species occurrences and environmental predictors-a phenomenon known as the modifiable area unit problem (MAUP), making model outputs directly contingent on the model resolution. Here, we test how model performance, predictor importance, and the spatial congruence of species predictions depend on the model resolution (i.e., average subcatchment size) of SDMs. We modeled the potential habitat suitability of 50 native fish species in the upper Danube catchment at 10 different model resolutions. Model resolutions were derived using a 90-m digital-elevation model by using the GRASS-GIS module r.watershed. Here, we decreased the average subcatchment size gradually from 632 to 2 km2. We then ran ensemble SDMs based on five algorithms using topographical, climatic, hydrological, and land-use predictors for each species and resolution. Model evaluation scores were consistently high, as sensitivity and True Skill Statistic values ranged from 86.1-93.2 and 0.61-0.73, respectively. The most contributing predictor changed from topography at coarse, to hydrology at fine resolutions. Climate predictors played an intermediate role for all resolutions, while land use was of little importance. Regarding the predicted habitat suitability, we identified a spatial filtering from coarse to intermediate resolutions. The predicted habitat suitability within a coarse resolution was not ported to all smaller, nested subcatchments, but only to a fraction that held the suitable environmental conditions. Across finer resolutions, the mapped predictions were spatially congruent without such filter effect. We show that freshwater SDM predictions can have consistently high evaluation scores while mapped predictions differ significantly and are highly contingent on the underlying subcatchment size. We encourage building freshwater SDMs across multiple catchment sizes, to assess model variability and uncertainties in model outcomes emerging from the MAUP.
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Affiliation(s)
- Martin Friedrichs‐Manthey
- Leibniz‐Institute of Freshwater Ecology and Inland FisheriesBerlinGermany
- Department of BiologyFreie Universität BerlinBerlinGermany
| | - Simone D. Langhans
- Department of ZoologyUniversity of OtagoDunedinNew Zealand
- BC3—Basque Centre for Climate ChangeLeioaSpain
| | - Thomas Hein
- Institute of Hydrobiology and Aquatic Ecosystem ManagementUniversity of Natural Resources and Life SciencesViennaAustria
- WasserCluster LunzLunzAustria
| | - Florian Borgwardt
- Institute of Hydrobiology and Aquatic Ecosystem ManagementUniversity of Natural Resources and Life SciencesViennaAustria
| | | | - Sonja C. Jähnig
- Leibniz‐Institute of Freshwater Ecology and Inland FisheriesBerlinGermany
| | - Sami Domisch
- Leibniz‐Institute of Freshwater Ecology and Inland FisheriesBerlinGermany
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Schmidt H, Radinger J, Teschlade D, Stoll S. The role of spatial units in modelling freshwater fish distributions: Comparing a subcatchment and river network approach using MaxEnt. Ecol Modell 2020. [DOI: 10.1016/j.ecolmodel.2020.108937] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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13
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Vassallo P, Paoli C, Aliani S, Cocito S, Morri C, Bianchi CN. Benthic diversity patterns and predictors: A study case with inferences for conservation. MARINE POLLUTION BULLETIN 2020; 150:110748. [PMID: 31784263 DOI: 10.1016/j.marpolbul.2019.110748] [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: 03/18/2019] [Revised: 09/08/2019] [Accepted: 11/18/2019] [Indexed: 06/10/2023]
Abstract
Understanding which drivers cause diversity patterns is a key issue in conservation. Here we applied a spatially explicit model to predict marine benthic diversity patterns according to environmental factors in the NW Mediterranean Sea. While most conservation-oriented diversity studies consider species richness only and neglect equitability, we measured separately species richness, equitability, and 'overall' diversity (i.e., the Shannon-Wiener H' function) on a dataset of 890 benthic species × 209 samples. Diversity values were predicted by means of Random Forest regression, on the basis of 10 factors: depth, distance from the coast, distance from the shelf break, latitude, sea-floor slope, sediment grain size, sediment sorting, distance from harbours and marinas, distance from rivers, and sampling gear. Predictions by Random Forests were accurate, the main predictors being latitude, sediment grain size, depth and distance from the coast. Based on predicted values, diversity hotspots were identified as those localities where indices were in the 15% top segment of ranked values. Only a minority of the diversity hotspots was included within the boundaries of the protection institutes established in the region. Marine protected areas are often created in sites harbouring important coastal habitats, which risks neglecting the diversity hidden in the sedimentary seafloor. We suggest that marine protected areas should accommodate portions of sedimentary habitat within their boundaries to improve diversity conservation.
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Affiliation(s)
- Paolo Vassallo
- DiSTAV (Department of Earth, Environmental and Life Sciences), University of Genoa, Corso Europa 26, I-16132 Genova, Italy
| | - Chiara Paoli
- DiSTAV (Department of Earth, Environmental and Life Sciences), University of Genoa, Corso Europa 26, I-16132 Genova, Italy
| | - Stefano Aliani
- ISMAR (Institute of Marine Sciences), CNR, Forte Santa Teresa, I-19036 Pozzuolo di Lerici, SP, Italy
| | - Silvia Cocito
- ENEA (Italian Agency for New Technologies, Energy and Sustainable Economic Development), Marine Environment Research Centre, I-19100 La Spezia, Italy
| | - Carla Morri
- DiSTAV (Department of Earth, Environmental and Life Sciences), University of Genoa, Corso Europa 26, I-16132 Genova, Italy
| | - Carlo Nike Bianchi
- DiSTAV (Department of Earth, Environmental and Life Sciences), University of Genoa, Corso Europa 26, I-16132 Genova, Italy.
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