Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data. Issue 7 (25th March 2018)

• Record Type: Journal Article
• Authors: Zeller, Katherine A.; Jennings, Megan K.; Vickers, T. Winston; Ernest, Holly B.; Cushman, Samuel A.; Boyce, Walter M.
• Editors: Bolliger, Janine
• Is Part Of: Diversity & distributions. Volume 24:Issue 7(2018)
• Page Start: 868
• Page End: 879
• Publication Date: 2018-03-25
• Subjects: circuit theory -- connectivity -- corridors -- cost distance -- dispersal -- Puma concolor; Biodiversity -- Periodicals; Biodiversity conservation -- Periodicals; 577
• DOI: 10.1111/ddi.12742
• Languages: English
• ISSNs: 1366-9516

Abstract

Abstract: Aim: There is enormous interest in applying connectivity modelling to resistance surfaces for identifying corridors for conservation action. However, the multiple analytical approaches used to estimate resistance surfaces and predict connectivity across resistance surfaces have not been rigorously compared, and it is unclear what methods provide the best inferences about population connectivity. Using a large empirical data set on puma ( Puma concolor ), we are the first to compare several of the most common approaches for estimating resistance and modelling connectivity and validate them with dispersal data. Location: Southern California, USA. Methods: We estimate resistance using presence‐only data, GPS telemetry data from puma home ranges and genetic data using a variety of analytical methods. We model connectivity with cost distance and circuit theory algorithms. We then measure the ability of each data type and connectivity algorithm to capture GPS telemetry points of dispersing pumas. Results: We found that resource selection functions based on GPS telemetry points and paths outperformed species distribution models when applied using cost distance connectivity algorithms. Point and path selection functions were not statistically different in their performance, but point selection functions were more sensitive to the transformation used to convert relative probability of use to resistance. Point and path selection functions and landscape genetics outperformed other methods when applied with cost distance; no methods outperformed one another with circuit theory. Main conclusions: We conclude that path or point selection functions, or landscape genetic models, should be used to estimate landscape resistance for wildlife. In cases where resource limitations prohibit the collection of GPS collar or genetic data, our results suggest that species distribution models, while weaker, may still be sufficient for resistance estimation. We recommend the use of cost distance‐based approaches, such as least‐cost corridors and resistant kernels, for estimating connectivity and identifying functional corridors for terrestrial wildlife.