Uncertainties in the identification of potential dispersal corridors: The importance of behaviour, sex, and algorithm

Abstract Modelling landscape connectivity represents one of the central challenges for conservation of natural resources, especially in human dominated landscapes. Many different methods have been developed to this effect, but their assumptions and limitations have been largely ignored. Using high resolution GPS tracking data from brown bears (Ursus arctos) in central Italy, we investigated the influence of behavioural state (movement vs other behaviours), sex, and algorithms, namely least cost path and circuit theory, on the identification of structural corridors. In particular, considering that most studies does not account for behavioural states and/or individual characteristics, and that basically all studies consider only a single corridor algorithm, we performed (1) a within-algorithm comparison, under the hypothesis that both behavioural states and sex would influence prediction of structural corridors, and (2) a between-algorithm comparison, under the hypothesis that different algorithms would predict different corridors. We found that the impact of sex and/or behavioural state was substantial. On average, least cost path corridors for moving females were 4.7 km apart (st.dev = 7.6 km) from corridors for moving males, and 5.0 km apart (st.dev = 7.2 km) from corridors not considering sex and behaviour. The same was true for circuit theory corridors. The between-algorithm comparison showed that the two corridor models yielded almost identical results, with >80% of the least cost path corridors falling into the two top deciles for the corresponding circuit theory corridors. Our results suggest that the failure to consider an animal’s behavioural state and/or intraspecific differences may result in misidentification of corridors, with potential misallocation of the limited conservation resources available.

[1]  Mark S Boyce,et al.  Applications of step-selection functions in ecology and conservation , 2014, Movement Ecology.

[2]  M. Boyce,et al.  WOLVES INFLUENCE ELK MOVEMENTS: BEHAVIOR SHAPES A TROPHIC CASCADE IN YELLOWSTONE NATIONAL PARK , 2005 .

[3]  C. Patrick Doncaster,et al.  Evaluating least-cost model predictions with empirical dispersal data: A case-study using radiotracking data of hedgehogs (Erinaceus europaeus) , 2007 .

[4]  Juan M. Morales,et al.  EXTRACTING MORE OUT OF RELOCATION DATA: BUILDING MOVEMENT MODELS AS MIXTURES OF RANDOM WALKS , 2004 .

[5]  Paul Beier,et al.  The Role of Landscape Connectivity in Planning and Implementing Conservation and Restoration Priorities. Issues in Ecology , 2012 .

[6]  V. Radeloff,et al.  Assessing differences in connectivity based on habitat versus movement models for brown bears in the Carpathians , 2016, Landscape Ecology.

[7]  Sandro Lovari,et al.  A habitat model for brown bear conservation and land use planning in the central Apennines , 2004 .

[8]  Erin L. Koen,et al.  Landscape connectivity for wildlife: development and validation of multispecies linkage maps , 2014 .

[9]  A. Zedrosser,et al.  Should I stay or should I go? Natal dispersal in the brown bear , 2007, Animal Behaviour.

[10]  Samuel A. Cushman,et al.  Movement behavior explains genetic differentiation in American black bears , 2010, Landscape Ecology.

[11]  Kevin McGarigal,et al.  Sensitivity of landscape resistance estimates based on point selection functions to scale and behavioral state: pumas as a case study , 2014, Landscape Ecology.

[12]  Nachum Ulanovsky,et al.  Large-scale navigational map in a mammal , 2011, Proceedings of the National Academy of Sciences.

[13]  Mark New,et al.  Ensemble forecasting of species distributions. , 2007, Trends in ecology & evolution.

[14]  Alan M. Wilson,et al.  Does wildlife resource selection accurately inform corridor conservation , 2017 .

[15]  Alan M. Wilson,et al.  Lessons from integrating behaviour and resource selection: activity‐specific responses of African wild dogs to roads , 2016 .

[16]  Mark S. Boyce,et al.  Corridors for Conservation: Integrating Pattern and Process , 2006 .

[17]  Peter Vogt,et al.  Mapping functional connectivity , 2009 .

[18]  L. Maiorano,et al.  Ecological Networks as Conceptual Frameworks or Operational Tools in Conservation , 2007, Conservation biology : the journal of the Society for Conservation Biology.

[19]  Luigi Boitani,et al.  Gap analysis of terrestrial vertebrates in Italy: Priorities for conservation planning in a human dominated landscape , 2006 .

[20]  L. Maiorano,et al.  Changes in land-use/land-cover patterns in Italy and their implications for biodiversity conservation , 2007, Landscape Ecology.

[21]  L. Fahrig.  Effects of Habitat Fragmentation on Biodiversity , 2003 .

[22]  R. Kays,et al.  Animal behavior, cost-based corridor models, and real corridors , 2013, Landscape Ecology.

[23]  Mark S. Boyce,et al.  Use of resource selection functions to identify conservation corridors , 2009 .

[24]  Kevin McGarigal,et al.  Using step and path selection functions for estimating resistance to movement: pumas as a case study , 2015, Landscape Ecology.

[25]  Carrie A. Schloss,et al.  Dispersal will limit ability of mammals to track climate change in the Western Hemisphere , 2012, Proceedings of the National Academy of Sciences.

[26]  L. Maiorano,et al.  Land-cover change and the future of the Apennine brown bear: a perspective from the past , 2008 .

[27]  K. Beard,et al.  A Meta‐Analytic Review of Corridor Effectiveness , 2010, Conservation biology : the journal of the Society for Conservation Biology.

[28]  Dean L. Urban,et al.  Comparing Habitat Suitability and Connectivity Modeling Methods for Conserving Pronghorn Migrations , 2012, PloS one.

[29]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[30]  Jonathan R Potts,et al.  Predicting local and non-local effects of resources on animal space use using a mechanistic step selection model , 2014, Methods in ecology and evolution.

[31]  Marco Saerens,et al.  Predicting the continuum between corridors and barriers to animal movements using Step Selection Functions and Randomized Shortest Paths. , 2016, The Journal of animal ecology.

[32]  A. Hansen,et al.  Connecting models to movements: testing connectivity model predictions against empirical migration and dispersal data , 2016, Landscape Ecology.

[33]  Modeling the distribution of Apennine brown bears during hyperphagia to reduce the impact of wild boar hunting , 2015, European Journal of Wildlife Research.

[34]  Viral B. Shah,et al.  Using circuit theory to model connectivity in ecology, evolution, and conservation. , 2008, Ecology.

[35]  D. Theobald,et al.  Achieving climate connectivity in a fragmented landscape , 2016, Proceedings of the National Academy of Sciences of the United States of America.

[36]  K. Rode,et al.  Sexual dimorphism, reproductive strategy, and human activities determine resource use by brown bears. , 2006, Ecology.

[37]  Vilis O Nams,et al.  Improving Accuracy and Precision in Estimating Fractal Dimension of Animal movement paths , 2006, Acta biotheoretica.

[38]  Lael Parrott,et al.  Distributed under Creative Commons Cc-by 4.0 an Individual-based Modelling Approach to Estimate Landscape Connectivity for Bighorn Sheep (ovis Canadensis) , 2022 .

[39]  Erik Matthysen,et al.  The application of 'least-cost' modelling as a functional landscape model , 2003 .

[40]  L. Boitani,et al.  The Apennine Brown Bear: A Critical Review of Its Status and Conservation Problems , 2008 .

[41]  Luigi Boitani,et al.  Contribution of the Natura 2000 Network to Biodiversity Conservation in Italy , 2007, Conservation biology : the journal of the Society for Conservation Biology.

[42]  M. Araújo,et al.  BIOMOD – a platform for ensemble forecasting of species distributions , 2009 .

[43]  Paul Beier,et al.  Estimating landscape resistance from habitat suitability: effects of data source and nonlinearities , 2016, Landscape Ecology.

[44]  M. Fortin,et al.  EDITOR'S CHOICE: Stepping stones are crucial for species' long‐distance dispersal and range expansion through habitat networks , 2014 .

[45]  D. Macdonald,et al.  The devil is in the dispersers: predictions of landscape connectivity change with demography , 2014 .

[46]  Kevin McGarigal,et al.  Estimating landscape resistance to movement: a review , 2012, Landscape Ecology.

[47]  Sean A. Parks,et al.  Combining resource selection and movement behavior to predict corridors for Canada lynx at their southern range periphery , 2013 .

[48]  Dean P. Anderson,et al.  State-space models link elk movement patterns to landscape characteristics in Yellowstone National Park , 2007 .

[49]  S. Cushman,et al.  A comparative framework to infer landscape effects on population genetic structure: are habitat suitability models effective in explaining gene flow? , 2015, Landscape Ecology.