Modelling density surfaces of intraspecific classes using camera trap distance sampling

Abstract Spatially explicit densities of wildlife are important for understanding environmental drivers of populations, and density surfaces of intraspecific classes allow exploration of links between demographic ratios and environmental conditions. Although spatially explicit densities and class de...

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Main Authors: Zackary J. Delisle, David L. Miller, Robert K. Swihart
Format: Article
Language:English
Published: Wiley 2023-05-01
Series:Methods in Ecology and Evolution
Subjects:
Online Access:https://doi.org/10.1111/2041-210X.14093
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author Zackary J. Delisle
David L. Miller
Robert K. Swihart
author_facet Zackary J. Delisle
David L. Miller
Robert K. Swihart
author_sort Zackary J. Delisle
collection DOAJ
description Abstract Spatially explicit densities of wildlife are important for understanding environmental drivers of populations, and density surfaces of intraspecific classes allow exploration of links between demographic ratios and environmental conditions. Although spatially explicit densities and class densities are valuable, conventional design‐based estimators remain prevalent when using camera‐trapping methods for unmarked populations. We developed a density surface model that utilized camera trap distance sampling data within a hierarchical generalized additive modelling framework. We estimated density surfaces of intraspecific classes of a common ungulate, white‐tailed deer Odocoileus virginianus, across three large management regions in Indiana, United States. We then extended simple statistical theory to test for differences in two ratios of density. Deer density was influenced by landscape fragmentation, wetlands and anthropogenic development. We documented class‐specific responses of density to availability of concealment cover, and found strong evidence that increased recruitment of young was tied to increased resource availability from anthropogenic agricultural land use. The coefficients of variation of the total density estimates within the three regions we surveyed were 0.11, 0.10 and 0.06. Synthesis and applications. Our strategy extends camera trap distance sampling and enables managers to use camera traps to better understand spatial predictors of density. Our density estimates were more precise than previous estimates from camera trap distance sampling. Population managers can use our methods to detect finer spatiotemporal changes in density or ratios of intraspecific‐class densities. Such changes in density can be linked to land use, or to management regimes on habitat and harvest limits of game species.
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spelling doaj.art-6030febafa2945969f432a86a50345fb2023-08-01T18:55:36ZengWileyMethods in Ecology and Evolution2041-210X2023-05-011451287129810.1111/2041-210X.14093Modelling density surfaces of intraspecific classes using camera trap distance samplingZackary J. Delisle0David L. Miller1Robert K. Swihart2Department of Forestry and Natural Resources Purdue University West Lafayette Indiana USABiomathematics and Statistics Scotland Dundee ScotlandDepartment of Forestry and Natural Resources Purdue University West Lafayette Indiana USAAbstract Spatially explicit densities of wildlife are important for understanding environmental drivers of populations, and density surfaces of intraspecific classes allow exploration of links between demographic ratios and environmental conditions. Although spatially explicit densities and class densities are valuable, conventional design‐based estimators remain prevalent when using camera‐trapping methods for unmarked populations. We developed a density surface model that utilized camera trap distance sampling data within a hierarchical generalized additive modelling framework. We estimated density surfaces of intraspecific classes of a common ungulate, white‐tailed deer Odocoileus virginianus, across three large management regions in Indiana, United States. We then extended simple statistical theory to test for differences in two ratios of density. Deer density was influenced by landscape fragmentation, wetlands and anthropogenic development. We documented class‐specific responses of density to availability of concealment cover, and found strong evidence that increased recruitment of young was tied to increased resource availability from anthropogenic agricultural land use. The coefficients of variation of the total density estimates within the three regions we surveyed were 0.11, 0.10 and 0.06. Synthesis and applications. Our strategy extends camera trap distance sampling and enables managers to use camera traps to better understand spatial predictors of density. Our density estimates were more precise than previous estimates from camera trap distance sampling. Population managers can use our methods to detect finer spatiotemporal changes in density or ratios of intraspecific‐class densities. Such changes in density can be linked to land use, or to management regimes on habitat and harvest limits of game species.https://doi.org/10.1111/2041-210X.14093abundancedeerdensity surface modellinggeneralized additive modelprecisionrecruitment
spellingShingle Zackary J. Delisle
David L. Miller
Robert K. Swihart
Modelling density surfaces of intraspecific classes using camera trap distance sampling
Methods in Ecology and Evolution
abundance
deer
density surface modelling
generalized additive model
precision
recruitment
title Modelling density surfaces of intraspecific classes using camera trap distance sampling
title_full Modelling density surfaces of intraspecific classes using camera trap distance sampling
title_fullStr Modelling density surfaces of intraspecific classes using camera trap distance sampling
title_full_unstemmed Modelling density surfaces of intraspecific classes using camera trap distance sampling
title_short Modelling density surfaces of intraspecific classes using camera trap distance sampling
title_sort modelling density surfaces of intraspecific classes using camera trap distance sampling
topic abundance
deer
density surface modelling
generalized additive model
precision
recruitment
url https://doi.org/10.1111/2041-210X.14093
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