Strong inference from transect sign surveys : combining spatial autocorrelation and misclassification occupancy models to quantify the detectability of a recovering carnivore

Acknowledgements We are very grateful for the input provided by Elizabeth Croose, Declan O'Mahony and Denise O'Meara on pine marten survey methodology and related constraints, which we hope this paper will go some way toward relieving. Christopher Sutherland was incredibly helpful in discussion of occupancy modelling techniques. We would also like to thank Thys Simpson, Colin McClean and Shaila Rao for arranging access to private estates for surveying. Funding — Forest Enterprise Scotland and the University of Aberdeen provided funding for the project. The Carnegie Trust supported the lead author, E. McHenry, in this research through the award of a tuition fees bursary.Peer reviewedPublisher PD

A mechanistic approach to weighting edge-effects in landscape connectivity assessments

Context: Understanding landscape functional connectivity is critical for nature conservation in fragmented landscapes. Spatially explicit graph-theoretical approaches to assessing landscape connectivity have provided a promising framework for capturing functional components driving connectivity at the landscape scale. However, existing weighting schemes used to parameterise functional connectivity in graph theory-based methods are limited with respect to their ability to capture patch-level characteristics relevant to habitat use such as edge-effects. Objectives: We set out to develop a new approach to weighting habitat connectivity as a function of edge-effects exerted by non-habitat patches through better delineation of edge-interior habitat transitions at the patch-level and parameterization of intra-patch movement cost at the landscape scale. Methods: We leverage the use of raster surfaces and area-weighted exponential kernels to operationalize a mechanistic approach to computing spatially explicit edge surfaces. We integrate map algebra, graph theory and landscape resistance methods to capture connectivity for a range of species specialisms on the edge-interior spectrum. We implement our method through a set of functions in the R statistical environment. Result: Through a real-world case study, we demonstrate that our approach, drawing on these behaviours, outperforms competing metrics when evaluating potential functional connectivity in a typically fragmented agricultural landscape. We highlight options for the optimal parameterization of graph-theoretical models. Conclusion: Our method offers increased flexibility, being tuneable for interior-edge habitat transitions. This therefore represents a key opportunity that can help to re-align the fields of landscape ecology and conservation biology by reconciling patch-versus-landscape methodological stances

Advances in Landscape Connectivity Assessment for Species Conservation

Metrics of functional connectivity are necessary to understand the influence of habitat loss and fragmentation on biodiversity outcomes. Effective metrics must capture three landscape characteristics: i) habitat availability, ii) probability of movement between habitat patches and iii) habitat quality. Patch area has generally been used as a surrogate for quality such that a bias towards fewer, larger patches exists in connectivity research (mirrored across conservation science). We argue that this approach neglects species of conservation concern in highly fragmented landscapes that may persist where dispersal and habitat availability override minimum patch size requirements. We provide solutions to address this bia

A mechanistic approach to weighting edge-effects in landscape connectivity assessments

ContextUnderstanding landscape functional connectivity is critical for nature conservation in fragmented landscapes. Spatially explicit graph-theoretical approaches to assessing landscape connectivity have provided a promising framework for capturing functional components driving connectivity at the landscape scale. However, existing weighting schemes used to parameterise functional connectivity in graph theory-based methods are limited with respect to their ability to capture patch-level characteristics relevant to habitat use such as edge-effects. ObjectivesWe set out to develop a new approach to weighting habitat connectivity as a function of edge-effects exerted by non-habitat patches through better delineation of edge-interior habitat transitions at the patch-level and parameterization of intra-patch movement cost at the landscape scale.MethodsWe leverage the use of raster surfaces and area-weighted exponential kernels to operationalize a mechanistic approach to computing spatially explicit edge surfaces. We integrate map algebra, graph theory and landscape resistance methods to capture connectivity for a range of species specialisms on the edge-interior spectrum. We implement our method through a set of functions in the R statistical environment.ResultThrough a real-world case study, we demonstrate that our approach, drawing on these behaviours, outperforms competing metrics when evaluating potential functional connectivity in a typically fragmented agricultural landscape. We highlight options for the optimal parameterization of graph-theoretical models. ConclusionOur method offers increased flexibility, being tuneable for interior-edge habitat transitions. This therefore represents a key opportunity that can help to re-align the fields of landscape ecology and conservation biology by reconciling patch-versus-landscape methodological stances. <br/