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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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Carroll SL, Schmidt GM, Waller JS, Graves TA. Evaluating density-weighted connectivity of black bears (Ursus americanus) in Glacier National Park with spatial capture-recapture models. MOVEMENT ECOLOGY 2024; 12:8. [PMID: 38263096 PMCID: PMC11334611 DOI: 10.1186/s40462-023-00445-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 12/19/2023] [Indexed: 01/25/2024]
Abstract
BACKGROUND Improved understanding of wildlife population connectivity among protected area networks can support effective planning for the persistence of wildlife populations in the face of land use and climate change. Common approaches to estimating connectivity often rely on small samples of individuals without considering the spatial structure of populations, leading to limited understanding of how individual movement links to demography and population connectivity. Recently developed spatial capture-recapture (SCR) models provide a framework to formally connect inference about individual movement, connectivity, and population density, but few studies have applied this approach to empirical data to support connectivity planning. METHODS We used mark-recapture data collected from 924 genetic detections of 598 American black bears (Ursus americanus) in 2004 with SCR ecological distance models to simultaneously estimate density, landscape resistance to movement, and population connectivity in Glacier National Park northwest Montana, USA. We estimated density and movement parameters separately for males and females and used model estimates to calculate predicted density-weighted connectivity surfaces. RESULTS Model results indicated that landscape structure influences black bear density and space use in Glacier. The mean density estimate was 16.08 bears/100 km2 (95% CI 12.52-20.6) for females and 9.27 bears/100 km2 (95% CI 7.70-11.14) for males. Density increased with forest cover for both sexes. For male black bears, density decreased at higher grizzly bear (Ursus arctos) densities. Drainages, valley bottoms, and riparian vegetation decreased estimates of landscape resistance to movement for male and female bears. For males, forest cover also decreased estimated resistance to movement, but a transportation corridor bisecting the study area strongly increased resistance to movement presenting a barrier to connectivity. CONCLUSIONS Density-weighed connectivity surfaces highlighted areas important for population connectivity that were distinct from areas with high potential connectivity. For black bears in Glacier and surrounding landscapes, consideration of both vegetation and valley topography could inform the placement of underpasses along the transportation corridor in areas characterized by both high population density and potential connectivity. Our study demonstrates that the SCR ecological distance model can provide biologically realistic, spatially explicit predictions to support movement connectivity planning across large landscapes.
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Affiliation(s)
- Sarah L Carroll
- Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, 80523, USA.
| | - Greta M Schmidt
- Department of Biology, San Diego State University, San Diego, CA, 92182, USA
| | - John S Waller
- Glacier National Park, P.O. Box 128, West Glacier, MT, 59936, USA
| | - Tabitha A Graves
- U.S. Geological Survey, Northern Rocky Mountain Science Center, PO Box 169, West Glacier, MT, 59936, USA
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