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Identifying conservation priorities in a defaunated tropical biodiversity hotspot

Citation

Tilker, Andrew et al. (2020), Identifying conservation priorities in a defaunated tropical biodiversity hotspot, Dryad, Dataset, https://doi.org/10.5061/dryad.ns1rn8pnx

Abstract

Aim: Unsustainable hunting is leading to widespread defaunation across the tropics. To mitigate against this threat with limited conservation resources, stakeholders must make decisions on where to focus anti-poaching activities. Identifying priority areas in a robust way allows decision-makers to target areas of conservation importance, therefore maximizing the impact of conservation interventions.

Location: Annamite mountains, Vietnam and Laos.

Methods: We conducted systematic landscape-scale surveys across five study sites (four protected areas, one unprotected area) using camera-trapping and leech-derived environmental DNA. We analyzed detections within a Bayesian multi-species occupancy framework to evaluate species responses to environmental and anthropogenic influences. Species responses were then used to predict occurrence to unsampled regions. We used predicted species richness maps and occurrence of endemic species to identify areas of conservation importance for targeted conservation interventions.

Results: Analyses showed that habitat-based covariates were uninformative. Our final model therefore incorporated three anthropogenic covariates as well as elevation, which reflects both ecological and anthropogenic factors. Conservation-priority species tended to found in areas that are more remote now or have been less accessible in the past, and at higher elevations. Predicted species richness was low and broadly similar across the sites, but slightly higher in the more remote site. Occupancy of the three endemic species showed a similar trend.

Main conclusion: Identifying spatial patterns of biodiversity in heavily-defaunated landscapes may require novel methodological and analytical approaches. Our results indicate to build robust prediction maps it is beneficial to sample over large spatial scales, use multiple detection methods to increase detections for rare species, include anthropogenic covariates that capture different aspects of hunting pressure, and analyze data within a Bayesian multi-species framework. Our models further suggest that more remote areas should be prioritized for anti-poaching efforts to prevent the loss of rare and endemic species.

Usage Notes

Download the Run_model.R script, the com_occ_model_FULL.txt and the compressed input_files.zip into your desired local drive.

Unzip the input_files folder. Please note that due to conservation issues we do not provide real species names in the input data.

Open the Run_model.R script and define an existing directory to save model output (second line). Define also the path to the input files (capture histories, effort matrices etc.). If you set those two parameters you should be able to reproduce our results with the data from the input_files folder.