10.5061/DRYAD.H4TN7
Pitman, Ross T.
Panthera Corporation
University of KwaZulu-Natal
Fattebert, Julien
Panthera Corporation
University of KwaZulu-Natal
Williams, Samual T.
Durham University
Williams, Kathryn S.
Durham University
Hill, Russell A.
Durham University
Hunter, Luke T. B.
Panthera Corporation
University of KwaZulu-Natal
Robinson, Hugh
University of Montana
Panthera Corporation
Power, John
Panthera Corporation
Swanepoel, Lourens
University of Venda
Slotow, Rob
University College London
University of KwaZulu-Natal
Balme, Guy A.
University of Cape Town
Panthera Corporation
Data from: Cats, connectivity and conservation: incorporating datasets and
integrating scales for wildlife management
Dryad
dataset
2016
leopard
circuit-theory
Permeability
Panthera pardus
land-use planning
Occupancy Modelling
conductance
landscape resistance
2016-12-13T14:27:38Z
2016-12-13T14:27:38Z
en
https://doi.org/10.1111/1365-2664.12851
45515967 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Understanding resource selection and quantifying habitat connectivity are
fundamental to conservation planning for both land-use and species
management plans. However, datasets available to management authorities
for resource selection and connectivity analyses are often highly limited
and fragmentary. As a result, measuring connectivity is challenging, and
often poorly integrated within conservation planning and wildlife
management. To exacerbate the challenge, scale-dependent resource use
makes inference across scales problematic, resource use is often modelled
in areas where the species is not present, and connectivity is typically
measured using a source-to-sink approach, erroneously assuming animals
possess predefined destinations. Here, we used a large carnivore, the
leopard Panthera pardus, to characterise resource use and landscape
connectivity across a vast, biodiverse region of southern Africa. Using a
range of datasets to counter data deficiencies inherent in carnivore
management, we overcame methodological limitations by employing occupancy
modelling and resource selection functions across three orders of
selection, and estimated landscape-scale habitat connectivity –
independent of a priori source and sink locations – using circuit theory.
We evaluated whether occupancy modelling on its own was capable of
accurately informing habitat connectivity, and identified conservation
priorities necessary for applied management. We detected markedly
different scale-dependent relationships across all selection orders. Our
multi-data, multi-scale approach accurately predicted resource use across
multiple scales and demonstrates how management authorities can more
suitably utilise fragmentary datasets. We further developed an unbiased
landscape-scale depiction of habitat connectivity, and identified key
linkages in need of targeted management. We did not find support for the
use of occupancy modelling as a proxy for landscape-scale habitat
connectivity and further caution its use within a management context.
Synthesis and applications. Maintaining habitat connectivity remains a
fundamental component of wildlife management and conservation, yet data to
inform these biological and ecological processes are often scarce. We
present a robust approach that incorporates multi-scale fragmentary
datasets (e.g. mortality data, permit data, sightings data), routinely
collected by management authorities, to inform wildlife management and
land-use planning. We recommend that management authorities employ a
multi-data, multi-scale connectivity approach—as we present here—to
identify management units at risk of low connectivity.
First-order DataframeRaw occupancy data (first-order scale of selection)
for leopards Panthera
pardus.Dryad_Pitman_et.al_2016_First_Order_Dataframes.RdataSecond-order
DataframeRaw logistic regression data (second-order scale of selection)
for leopards Panthera
pardus.Dryad_Pitman_et.al_2016_Second_Order_Dataframe.RdataThird-order
DataframeRaw logistic regression data (third-order scale of selection) for
leopards Panthera
pardus.Dryad_Pitman_et.al_2016_Third_Order_Dataframe.Rdata
Southern Africa