@phdthesis{Ullmann2018,
  author    = {Ullmann, Wiebke},
  title     = {Understanding animal movement behaviour in dynamic agricultural landscapes},
  doi       = {10.25932/publishup-42715},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-427153},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vii, 183},
  year      = {2018},
  abstract  = {The movement of organisms has formed our planet like few other processes. Movements shape populations, communities, entire ecosystems, and guarantee fundamental ecosystem functions and services, like seed dispersal and pollination. Global, regional and local anthropogenic impacts influence animal movements across ecosystems all around the world. In particular, land-use modification, like habitat loss and fragmentation disrupt movements between habitats with profound consequences, from increased disease transmissions to reduced species richness and abundance. However, neither the influence of anthropogenic change on animal movement processes nor the resulting effects on ecosystems are well understood. Therefore, we need a coherent understanding of organismal movement processes and their underlying mechanisms to predict and prevent altered animal movements and their consequences for ecosystem functions. In this thesis I aim at understanding the influence of anthropogenically caused land-use change on animal movement processes and their underlying mechanisms. In particular, I am interested in the synergistic influence of large-scale landscape structure and fine-scale habitat features on basic-level movement behaviours (e.g. the daily amount of time spend running, foraging, and resting) and their emerging higher-level movements (home range formation). Based on my findings, I identify the likely consequences of altered animal movements that lead to the loss of species richness and abundances. The study system of my thesis are hares in agricultural landscapes. European brown hares (Lepus europaeus) are perfectly suited to study animal movements in agricultural landscapes, as hares are hermerophiles and prefer open habitats. They have historically thrived in agricultural landscapes, but their numbers are in decline. Agricultural areas are undergoing strong land-use changes due to increasing food demand and fast developing agricultural technologies. They are already the largest land-use class, covering 38\% of the world's terrestrial surface. To consider the relevance of a given landscape structure for animal movement behaviour I selected two differently structured agricultural landscapes - a simple landscape in Northern Germany with large fields and few landscape elements (e.g. hedges and tree stands), and a complex landscape in Southern Germany with small fields and many landscape elements. I applied GPS devices (hourly fixes) with internal high-resolution accelerometers (4 min samples) to track hares, receiving an almost continuous observation of the animals' behaviours via acceleration analyses. I used the spatial and behavioural information in combination with remote sensing data (normalized difference vegetation index, or NDVI, a proxy for resource availability), generating an almost complete idea of what the animal was doing when, why and where. Apart from landscape structure (represented by the two differently structured study areas), I specifically tested whether the following fine-scale habitat features influence animal movements: resource, agricultural management events, habitat diversity, and habitat structure. My results show that, irrespective of the movement process or mechanism and the type of fine-scale habitat features, landscape structure was the overarching variable influencing hare movement behaviour. High resource variability forces hares to enlarge their home ranges, but only in the simple and not in the complex landscape. Agricultural management events result in home range shifts in both landscapes, but force hares to increase their home ranges only in the simple landscape. Also the preference of habitat patches with low vegetation and the avoidance of high vegetation, was stronger in the simple landscape. High and dense crop fields restricted hare movements temporarily to very local and small habitat patch remnants. Such insuperable barriers can separate habitat patches that were previously connected by mobile links. Hence, the transport of nutrients and genetic material is temporarily disrupted. This mechanism is also working on a global scale, as human induced changes from habitat loss and fragmentation to expanding monocultures cause a reduction in animal movements worldwide. The mechanisms behind those findings show that higher-level movements, like increasing home ranges, emerge from underlying basic-level movements, like the behavioural modes. An increasing landscape simplicity first acts on the behavioural modes, i.e. hares run and forage more, but have less time to rest. Hence, the emergence of increased home range sizes in simple landscapes is based on an increased proportion of time running and foraging, largely due to longer travelling times between distant habitats and scarce resource items in the landscape. This relationship was especially strong during the reproductive phase, demonstrating the importance of high-quality habitat for reproduction and the need to keep up self-maintenance first, in low quality areas. These changes in movement behaviour may release a cascade of processes that start with more time being allocated to running and foraging, resulting into an increased energy expenditure and may lead to a decline in individual fitness. A decrease in individual fitness and reproductive output will ultimately affect population viability leading to local extinctions. In conclusion, I show that landscape structure has one of the most important effects on hare movement behaviour. Synergistic effects of landscape structure, and fine-scale habitat features, first affect and modify basic-level movement behaviours, that can scales up to altered higher-level movements and may even lead to the decline of species richness and abundances, and the disruption of ecosystem functions. Understanding the connection between movement mechanisms and processes can help to predict and prevent anthropogenically induced changes in movement behaviour. With regard to the paramount importance of landscape structure, I strongly recommend to decrease the size of agricultural fields and increase crop diversity. On the small-scale, conservation policies should assure the year round provision of areas with low vegetation height and high quality forage. This could be done by generating wildflower strips and additional (semi-) natural habitat patches. This will not only help to increase the populations of European brown hares and other farmland species, but also ensure and protects the continuity of mobile links and their intrinsic value for sustaining important ecosystem functions and services.},
  language  = {en}
}
@article{NoonanTuckerFlemingetal.2018,
  author    = {Noonan, Michael J. and Tucker, Marlee A. and Fleming, Christen H. and Akre, Thomas S. and Alberts, Susan C. and Ali, Abdullahi H. and Altmann, Jeanne and Antunes, Pamela Castro and Belant, Jerrold L. and Beyer, Dean and Blaum, Niels and Boehning-Gaese, Katrin and Cullen Jr, Laury and de Paula, Rogerio Cunha and Dekker, Jasja and Drescher-Lehman, Jonathan and Farwig, Nina and Fichtel, Claudia and Fischer, Christina and Ford, Adam T. and Goheen, Jacob R. and Janssen, Rene and Jeltsch, Florian and Kauffman, Matthew and Kappeler, Peter M. and Koch, Flavia and LaPoint, Scott and Markham, A. Catherine and Medici, Emilia Patricia and Morato, Ronaldo G. and Nathan, Ran and Oliveira-Santos, Luiz Gustavo R. and Olson, Kirk A. and Patterson, Bruce D. and Paviolo, Agustin and Ramalho, Emiliano Estero and Rosner, Sascha and Schabo, Dana G. and Selva, Nuria and Sergiel, Agnieszka and da Silva, Marina Xavier and Spiegel, Orr and Thompson, Peter and Ullmann, Wiebke and Zieba, Filip and Zwijacz-Kozica, Tomasz and Fagan, William F. and Mueller, Thomas and Calabrese, Justin M.},
  title     = {A comprehensive analysis of autocorrelation and bias in home range estimation},
  series = {Ecological monographs : a publication of the Ecological Society of America.},
  volume    = {89},
  journal   = {Ecological monographs : a publication of the Ecological Society of America.},
  number    = {2},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0012-9615},
  doi       = {10.1002/ecm.1344},
  pages     = {21},
  year      = {2018},
  abstract  = {Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ( N̂ area ) to quantify the information content of each data set. We found that AKDE 95\% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the hold-out sets by AKDE 95\% (or 50\%) estimates was 95.3\% (or 50.1\%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing N̂ area. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small N̂ area. While 72\% of the 369 empirical data sets had >1,000 total observations, only 4\% had an N̂ area >1,000, where 30\% had an N̂ area <30. In this frequently encountered scenario of small N̂ area, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.},
  language  = {en}
}