@article{PottsSchlaegel2020,
  author    = {Potts, Jonathan R. and Schl{\"a}gel, Ulrike E.},
  title     = {Parametrizing diffusion-taxis equations from animal movement trajectories using step selection analysis},
  series = {Methods in ecology and evolution : an official journal of the British Ecological Society},
  volume    = {11},
  journal   = {Methods in ecology and evolution : an official journal of the British Ecological Society},
  number    = {9},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {2041-210X},
  doi       = {10.1111/2041-210X.13406},
  pages     = {1092 -- 1105},
  year      = {2020},
  abstract  = {Mathematical analysis of partial differential equations (PDEs) has led to many insights regarding the effect of organism movements on spatial population dynamics. However, their use has mainly been confined to the community of mathematical biologists, with less attention from statistical and empirical ecologists. We conjecture that this is principally due to the inherent difficulties in fitting PDEs to data. To help remedy this situation, in the context of movement ecology, we show how the popular technique of step selection analysis (SSA) can be used to parametrize a class of PDEs, calleddiffusion-taxismodels, from an animal's trajectory. We examine the accuracy of our technique on simulated data, then demonstrate the utility of diffusion-taxis models in two ways. First, for non-interacting animals, we derive the steady-state utilization distribution in a closed analytic form. Second, we give a recipe for deriving spatial pattern formation properties that emerge from interacting animals: specifically, do those interactions cause heterogeneous spatial distributions to emerge and if so, do these distributions oscillate at short times or emerge without oscillations? The second question is applied to data on concurrently tracked bank volesMyodes glareolus. Our results show that SSA can accurately parametrize diffusion-taxis equations from location data, providing the frequency of the data is not too low. We show that the steady-state distribution of our diffusion-taxis model, where it is derived, has an identical functional form to the utilization distribution given by resource selection analysis (RSA), thus formally linking (fine scale) SSA with (broad scale) RSA. For the bank vole data, we show how our SSA-PDE approach can give predictions regarding the spatial aggregation and segregation of different individuals, which are difficult to predict purely by examining results of SSA. Our methods provide a user-friendly way into the world of PDEs, via a well-used statistical technique, which should lead to tighter links between the findings of mathematical ecology and observations from empirical ecology. By providing a non-speculative link between observed movement behaviours and space use patterns on larger spatio-temporal scales, our findings will also aid integration of movement ecology into understanding spatial species distributions.},
  language  = {en}
}
@article{ZurellvonWehrdenRoticsetal.2018,
  author    = {Zurell, Damaris and von Wehrden, Henrik and Rotics, Shay and Kaatz, Michael and Gross, Helge and Schlag, Lena and Sch{\"a}fer, Merlin and Sapir, Nir and Turjeman, Sondra and Wikelski, Martin and Nathan, Ran and Jeltsch, Florian},
  title     = {Home range size and resource use of breeding and non-breeding white storks along a land use gradient},
  series = {Frontiers in Ecology and Evolution},
  volume    = {6},
  journal   = {Frontiers in Ecology and Evolution},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {2296-701X},
  doi       = {10.3389/fevo.2018.00079},
  pages     = {11},
  year      = {2018},
  abstract  = {Biotelemetry is increasingly used to study animal movement at high spatial and temporal resolution and guide conservation and resource management. Yet, limited sample sizes and variation in space and habitat use across regions and life stages may compromise robustness of behavioral analyses and subsequent conservation plans. Here, we assessed variation in (i) home range sizes, (ii) home range selection, and (iii) fine-scale resource selection of white storks across breeding status and regions and test model transferability. Three study areas were chosen within the Central German breeding grounds ranging from agricultural to fluvial and marshland. We monitored GPS-locations of 62 adult white storks equipped with solar-charged GPS/3D-acceleration (ACC) transmitters in 2013-2014. Home range sizes were estimated using minimum convex polygons. Generalized linear mixed models were used to assess home range selection and fine-scale resource selection by relating the home ranges and foraging sites to Corine habitat variables and normalized difference vegetation index in a presence/pseudo-absence design. We found strong variation in home range sizes across breeding stages with significantly larger home ranges in non-breeding compared to breeding white storks, but no variation between regions. Home range selection models had high explanatory power and well predicted overall density of Central German white stork breeding pairs. Also, they showed good transferability across regions and breeding status although variable importance varied considerably. Fine-scale resource selection models showed low explanatory power. Resource preferences differed both across breeding status and across regions, and model transferability was poor. Our results indicate that habitat selection of wild animals may vary considerably within and between populations, and is highly scale dependent. Thereby, home range scale analyses show higher robustness whereas fine-scale resource selection is not easily predictable and not transferable across life stages and regions. Such variation may compromise management decisions when based on data of limited sample size or limited regional coverage. We thus recommend home range scale analyses and sampling designs that cover diverse regional landscapes and ensure robust estimates of habitat suitability to conserve wild animal populations.},
  language  = {en}
}