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Prima MC, Garel M, Marchand P, Redcliffe J, Börger L, Barnier F. Combined effects of landscape fragmentation and sampling frequency of movement data on the assessment of landscape connectivity. MOVEMENT ECOLOGY 2024; 12:63. [PMID: 39252118 PMCID: PMC11385819 DOI: 10.1186/s40462-024-00492-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 07/10/2024] [Indexed: 09/11/2024]
Abstract
BACKGROUND Network theory is largely applied in real-world systems to assess landscape connectivity using empirical or theoretical networks. Empirical networks are usually built from discontinuous individual movement trajectories without knowing the effect of relocation frequency on the assessment of landscape connectivity while theoretical networks generally rely on simple movement rules. We investigated the combined effects of relocation sampling frequency and landscape fragmentation on the assessment of landscape connectivity using simulated trajectories and empirical high-resolution (1 Hz) trajectories of Alpine ibex (Capra ibex). We also quantified the capacity of commonly used theoretical networks to accurately predict landscape connectivity from multiple movement processes. METHODS We simulated forager trajectories from continuous correlated biased random walks in simulated landscapes with three levels of landscape fragmentation. High-resolution ibex trajectories were reconstructed using GPS-enabled multi-sensor biologging data and the dead-reckoning technique. For both simulated and empirical trajectories, we generated spatial networks from regularly resampled trajectories and assessed changes in their topology and information loss depending on the resampling frequency and landscape fragmentation. We finally built commonly used theoretical networks in the same landscapes and compared their predictions to actual connectivity. RESULTS We demonstrated that an accurate assessment of landscape connectivity can be severely hampered (e.g., up to 66% of undetected visited patches and 29% of spurious links) when the relocation frequency is too coarse compared to the temporal dynamics of animal movement. However, the level of landscape fragmentation and underlying movement processes can both mitigate the effect of relocation sampling frequency. We also showed that network topologies emerging from different movement behaviours and a wide range of landscape fragmentation were complex, and that commonly used theoretical networks accurately predicted only 30-50% of landscape connectivity in such environments. CONCLUSIONS Very high-resolution trajectories were generally necessary to accurately identify complex network topologies and avoid the generation of spurious information on landscape connectivity. New technologies providing such high-resolution datasets over long periods should thus grow in the movement ecology sphere. In addition, commonly used theoretical models should be applied with caution to the study of landscape connectivity in real-world systems as they did not perform well as predictive tools.
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Affiliation(s)
- Marie-Caroline Prima
- PatriNat (OFB - MNHN), 75005, Paris, France.
- Office Français de la Biodiversité, Direction de la Recherche et de l'Appui Scientifique, Service Anthropisation et Fonctionnement des Ecosystèmes Terrestres, 38610, Gières, France.
| | - Mathieu Garel
- Office Français de la Biodiversité, Direction de la Recherche et de l'Appui Scientifique, Service Anthropisation et Fonctionnement des Ecosystèmes Terrestres, 38610, Gières, France
| | - Pascal Marchand
- Office Français de la Biodiversité, Direction de la Recherche et de l'Appui Scientifique, Service Anthropisation et Fonctionnement des Ecosystèmes Terrestres, 34990, Juvignac, France
| | - James Redcliffe
- Department of Biosciences, Swansea University, Swansea, SA15HF, UK
| | - Luca Börger
- Department of Biosciences, Swansea University, Swansea, SA15HF, UK
- Centre for Biomathematics, Swansea University, Swansea, SA15HF, UK
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Gilbert NA, Amaral BR, Smith OM, Williams PJ, Ceyzyk S, Ayebare S, Davis KL, Leuenberger W, Doser JW, Zipkin EF. A century of statistical Ecology. Ecology 2024; 105:e4283. [PMID: 38738264 DOI: 10.1002/ecy.4283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/26/2023] [Accepted: 01/31/2024] [Indexed: 05/14/2024]
Abstract
As data and computing power have surged in recent decades, statistical modeling has become an important tool for understanding ecological patterns and processes. Statistical modeling in ecology faces two major challenges. First, ecological data may not conform to traditional methods, and second, professional ecologists often do not receive extensive statistical training. In response to these challenges, the journal Ecology has published many innovative statistical ecology papers that introduced novel modeling methods and provided accessible guides to statistical best practices. In this paper, we reflect on Ecology's history and its role in the emergence of the subdiscipline of statistical ecology, which we define as the study of ecological systems using mathematical equations, probability, and empirical data. We showcase 36 influential statistical ecology papers that have been published in Ecology over the last century and, in so doing, comment on the evolution of the field. As data and computing power continue to increase, we anticipate continued growth in statistical ecology to tackle complex analyses and an expanding role for Ecology to publish innovative and influential papers, advancing the discipline and guiding practicing ecologists.
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Affiliation(s)
- Neil A Gilbert
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
| | - Bruna R Amaral
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
| | - Olivia M Smith
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
- Center for Global Change and Earth Observations, Michigan State University, East Lansing, Michigan, USA
| | - Peter J Williams
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
| | - Sydney Ceyzyk
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
| | - Samuel Ayebare
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
| | - Kayla L Davis
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
| | - Wendy Leuenberger
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
| | - Jeffrey W Doser
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
| | - Elise F Zipkin
- Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
- Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
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Edelhoff H, Milleret C, Ebert C, Dupont P, Kudernatsch T, Zollner A, Bischof R, Peters W. Sexual segregation results in pronounced sex-specific density gradients in the mountain ungulate, Rupicapra rupicapra. Commun Biol 2023; 6:979. [PMID: 37749272 PMCID: PMC10520025 DOI: 10.1038/s42003-023-05313-z] [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: 08/12/2022] [Accepted: 09/01/2023] [Indexed: 09/27/2023] Open
Abstract
Sex-specific differences in habitat selection and space use are common in ungulates. Yet, it is largely unknown how this behavioral dimorphism, ultimately leading to sexual segregation, translates to population-level patterns and density gradients across landscapes. Alpine chamois (Rupicapra rupicapra r.) predominantly occupy habitat above tree line, yet especially males may also take advantage of forested habitats. To estimate male and female chamois density and determinants thereof, we applied Bayesian spatial capture-recapture (SCR) models in two contrasting study areas in the Alps, Germany, during autumn. We fitted SCR models to non-invasive individual encounter data derived from genotyped feces. Sex-specific densities were modeled as a function of terrain ruggedness, forest canopy cover, proportion of barren ground, and site severity. We detected pronounced differences in male and female density patterns, driven primarily by terrain ruggedness, rather than by sex-specific effects of canopy cover. The positive effect of ruggedness on density was weaker for males which translated into a higher proportion of males occupying less variable terrain, frequently located in forests, compared to females. By estimating sex-specific variation in both detection probabilities and density, we were able to quantify and map how individual behavioral differences scale up and shape spatial patterns in population density.
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Affiliation(s)
- Hendrik Edelhoff
- Wildlife Biology and Management Research Unit, Bavarian State Institute of Forestry, Freising, Germany.
| | - Cyril Milleret
- Faculty of Environmental Management and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
| | - Cornelia Ebert
- Seq-IT GmbH & Co.KG, Department Wildlife Genetics, Kaiserslautern, Germany
| | - Pierre Dupont
- Faculty of Environmental Management and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
| | - Thomas Kudernatsch
- Department of Conservation and Biodiversity, Bavarian State Institute of Forestry, Freising, Germany
| | - Alois Zollner
- Department of Conservation and Biodiversity, Bavarian State Institute of Forestry, Freising, Germany
| | - Richard Bischof
- Faculty of Environmental Management and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
| | - Wibke Peters
- Wildlife Biology and Management Research Unit, Bavarian State Institute of Forestry, Freising, Germany
- Wildlife Biology and Management Unit, Technical University of Munich, Freising, Germany
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Dupont PPA, Bischof R, Milleret C, Peters W, Edelhoff H, Ebert C, Klamm A, Hohmann U. An evaluation of spatial capture‐recapture models applied to ungulate non‐invasive genetic sampling data. J Wildl Manage 2023. [DOI: 10.1002/jwmg.22373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Affiliation(s)
- Pierre P. A. Dupont
- Faculty of Environmental Sciences and Natural Resource Management PB 5003, NO‐1432 Ås Norway
| | - Richard Bischof
- Faculty of Environmental Sciences and Natural Resource Management PB 5003, NO‐1432 Ås Norway
| | - Cyril Milleret
- Faculty of Environmental Sciences and Natural Resource Management PB 5003, NO‐1432 Ås Norway
| | - Wibke Peters
- Bavarian State Institute for Forestry Hans‐Carl‐von‐Carlowitzplatz 1 D‐85354 Freising Germany
| | - Hendrik Edelhoff
- Bavarian State Institute for Forestry Hans‐Carl‐von‐Carlowitzplatz 1 D‐85354 Freising Germany
| | - Cornelia Ebert
- Seq‐IT GmbH & Co. KG, Department of Wildlife Genetics Pfaffplatz 10 D‐67655 Kaiserslautern Germany
| | - Alisa Klamm
- Hainich National Park Bei der Marktkirche 9 D‐99947 Bad Langensalza Germany
| | - Ulf Hohmann
- Research Institute for Forest Ecology and Forestry Hauptstrasse 16 D‐67705 Trippstadt Germany
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Affiliation(s)
- Sarah J Converse
- U.S. Geological Survey, Washington Cooperative Fish and Wildlife Research Unit, School of Environmental and Forest Sciences & School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, USA
| | - Brett T McClintock
- Marine Mammal Laboratory, NOAA-NMFS Alaska Fisheries Science Center, Seattle, Washington, USA
| | - Paul B Conn
- Marine Mammal Laboratory, NOAA-NMFS Alaska Fisheries Science Center, Seattle, Washington, USA
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Gardner B, McClintock BT, Converse SJ, Hostetter NJ. Integrated animal movement and spatial capture-recapture models: Simulation, implementation, and inference. Ecology 2022; 103:e3771. [PMID: 35638187 PMCID: PMC9787507 DOI: 10.1002/ecy.3771] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 03/18/2022] [Accepted: 04/19/2022] [Indexed: 12/30/2022]
Abstract
Over the last decade, spatial capture-recapture (SCR) models have become widespread for estimating demographic parameters in ecological studies. However, the underlying assumptions about animal movement and space use are often not realistic. This is a missed opportunity because interesting ecological questions related to animal space use, habitat selection, and behavior cannot be addressed with most SCR models, despite the fact that the data collected in SCR studies - individual animals observed at specific locations and times - can provide a rich source of information about these processes and how they relate to demographic rates. We developed SCR models that integrated more complex movement processes that are typically inferred from telemetry data, including a simple random walk, correlated random walk (i.e., short-term directional persistence), and habitat-driven Langevin diffusion. We demonstrated how to formulate, simulate from, and fit these models with standard SCR data using data-augmented Bayesian analysis methods. We evaluated their performance through a simulation study, in which we varied the detection, movement, and resource selection parameters. We also examined different numbers of sampling occasions and assessed performance gains when including auxiliary location data collected from telemetered individuals. Across all scenarios, the integrated SCR movement models performed well in terms of abundance, detection, and movement parameter estimation. We found little difference in bias for the simple random walk model when reducing the number of sampling occasions from T = 25 to T = 15. We found some bias in movement parameter estimates under several of the correlated random walk scenarios, but incorporating auxiliary location data improved parameter estimates and significantly improved mixing during model fitting. The Langevin movement model was able to recover resource selection parameters from standard SCR data, which is particularly appealing because it explicitly links the individual-level movement process with habitat selection and population density. We focused on closed population models, but the movement models developed here can be extended to open SCR models. The movement process models could also be easily extended to accommodate additional "building blocks" of random walks, such as central tendency (e.g., territoriality) or multiple movement behavior states, thereby providing a flexible and coherent framework for linking animal movement behavior to population dynamics, density, and distribution.
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Affiliation(s)
- Beth Gardner
- School of Environmental and Forest SciencesUniversity of WashingtonSeattleWashingtonUSA
| | - Brett T. McClintock
- Marine Mammal LaboratoryNOAA‐NMFS Alaska Fisheries Science CenterSeattleWashingtonUSA
| | - Sarah J. Converse
- U.S. Geological Survey, Washington Cooperative Fish and Wildlife Research Unit, School of Environmental and Forest Sciences and School of Aquatic and Fishery SciencesUniversity of WashingtonSeattleWashingtonUSA
| | - Nathan J. Hostetter
- U.S. Geological Survey, North Carolina Cooperative Fish and Wildlife Research Unit, Department of Applied EcologyNorth Carolina State UniversityRaleighNorth CarolinaUSA
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