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Estimating statewide carrying capacity of bobcats (Lynx rufus) using improved maximum clique algorithms

Abstract

Context

Maximum clique analysis (MCA) can approximate landscape carrying capacity (Nk) for populations of territorial wildlife. However, MCA has not been widely adopted for wildlife applications, mainly due to computational constraints and software wildlife biologists may find difficult to use. Moreover, MCA does not incorporate uncertainty into estimates of Nk.

Objectives

We extended MCA by applying a vertex cover algorithm to compute Nk over a large (92,789 km2), continuous spatial scale for female bobcats (Lynx rufus) in Indiana, USA. We incorporated uncertainty by calculating confidence intervals for Nk across five thresholds of habitat suitability using 10 replicate suitability maps from bootstrapped datasets. For portions of the landscape too large to be solved with the vertex cover algorithm, we compared predictions from a linear model and a “greedy” algorithm.

Results

Mean estimates of Nk for female bobcats in Indiana across habitat suitability thresholds ranged from 539 (0.75 threshold) to 1200 territories (0.25 threshold). On average, each 12.5 percentile reduction in the suitability threshold increased estimates for Nk by 1.2-fold. Both the predictive and greedy algorithm produced reasonable estimates of maximum cliques for areas that were too large to compute with the vertex cover algorithm. The greedy algorithm produced smaller confidence intervals compared to the predictive approach but underestimated maximum cliques by 1.2%.

Conclusions

Our research demonstrates effective application of MCA to species occupying large landscapes while accounting for uncertainty. We believe our methods, coupled with availability of annotated scripts developed in R, will make MCA more broadly accessible to wildlife biologists.

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Code and Data Availability

Code and data for maximum clique analysis are shared publicly in the Purdue University repository (https://purr.purdue.edu/publications/4058/1).

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Acknowledgements

We thank all the volunteers, licensed trappers, deer archery hunters, Purdue University deer project technicians, DNR Furbearer program technicians, and Indiana Conservation Officers who helped collect bobcat location data. We thank Jacob Peterson for assistance with spatial analyses. We gratefully acknowledge the traditional homelands of the Indigenous People upon which Purdue University is built and our fieldwork was conducted. We honor and appreciate the Bodéwadmik (Potawatomi), Lenape (Delaware), Myaamia (Miami), and Shawnee People who are the original Indigenous caretakers.

Funding

This study was funded by Indiana Department of Natural Resources’ State Wildlife grant T3S series and Wildlife Restoration Grant W45R3, USDA National Institute of Food and Agriculture McIntire Stennis Project #1010322, Purdue University Department of Forestry and Natural Resources, and public donations to the Indiana Nongame Wildlife Fund.

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All authors contributed to study design and approved the final manuscript. GA, SJ, and CH collected the data. LJ conducted analyses, created code, and wrote the manuscript with contributions from RS and PZ. DG contributed computer algorithms and coding expertise.

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Correspondence to Landon R. Jones.

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Jones, L.R., Swihart, R.K., Gleich, D.F. et al. Estimating statewide carrying capacity of bobcats (Lynx rufus) using improved maximum clique algorithms. Landsc Ecol 37, 2383–2397 (2022). https://doi.org/10.1007/s10980-022-01460-6

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Keywords

  • Ensembles of small models
  • Habitat suitability
  • Home range capacity
  • Landscape carrying capacity
  • Maximum clique analysis
  • N k