@article{JeltschGrimmReegetal.2019, author = {Jeltsch, Florian and Grimm, Volker and Reeg, Jette and Schl{\"a}gel, Ulrike E.}, title = {Give chance a chance}, series = {Ecosphere}, volume = {10}, journal = {Ecosphere}, number = {5}, publisher = {ESA}, address = {Ithaca, NY}, issn = {2150-8925}, doi = {10.1002/ecs2.2700}, pages = {19}, year = {2019}, abstract = {A large part of biodiversity theory is driven by the basic question of what allows species to coexist in spite of a confined number of niches. A substantial theoretical background to this question is provided by modern coexistence theory (MCT), which rests on mathematical approaches of invasion analysis to categorize underlying mechanisms into factors that reduce either niche overlap (stabilizing mechanisms) or the average fitness differences of species (equalizing mechanisms). While MCT has inspired biodiversity theory in the search for these underlying mechanisms, we feel that the strong focus on coexistence causes a bias toward the most abundant species and neglects the plethora of species that are less abundant and often show high local turnover. Given the more stochastic nature of their occurrence, we advocate a complementary cross-level approach that links individuals, small populations, and communities and explicitly takes into account (1) a more complete inclusion of environmental and demographic stochasticity affecting small populations, (2) intraspecific trait variation and behavioral plasticity, and (3) local heterogeneities, interactions, and feedbacks. Focusing on mechanisms that drive the temporary coviability of species rather than infinite coexistence, we suggest a new approach that could be dubbed coviability analysis (CVA). From a modeling perspective, CVA builds on the merged approaches of individual-based modeling and population viability analysis but extends them to the community level. From an empirical viewpoint, CVA calls for a stronger integration of spatiotemporal data on variability and noise, changing drivers, and interactions at the level of individuals. The resulting large volumes of data from multiple sources could be strongly supported by novel techniques tailored to the discovery of complex patterns in high-dimensional data. By complementing MCT through a stronger focus on the coviability of less common species, this approach can help make modern biodiversity theory more comprehensive, predictive, and relevant for applications.}, language = {en} } @article{CrawfordJeltschMayetal.2018, author = {Crawford, Michael and Jeltsch, Florian and May, Felix and Grimm, Volker and Schl{\"a}gel, Ulrike E.}, title = {Intraspecific trait variation increases species diversity in a trait-based grassland model}, series = {Oikos}, volume = {128}, journal = {Oikos}, number = {3}, publisher = {Wiley}, address = {Hoboken}, issn = {0030-1299}, doi = {10.1111/oik.05567}, pages = {441 -- 455}, year = {2018}, abstract = {Intraspecific trait variation (ITV) is thought to play a significant role in community assembly, but the magnitude and direction of its influence are not well understood. Although it may be critical to better explain population persistence, species interactions, and therefore biodiversity patterns, manipulating ITV in experiments is challenging. We therefore incorporated ITV into a trait- and individual-based model of grassland community assembly by adding variation to the plants' functional traits, which then drive life-history tradeoffs. Varying the amount of ITV in the simulation, we examine its influence on pairwise-coexistence and then on the species diversity in communities of different initial sizes. We find that ITV increases the ability of the weakest species to invade most, but that this effect does not scale to the community level, where the primary effect of ITV is to increase the persistence and abundance of the competitively-average species. Diversity of the initial community is also of critical importance in determining ITV's efficacy; above a threshold of interspecific diversity, ITV does not increase diversity further. For communities below this threshold, ITV mainly helps to increase diversity in those communities that would otherwise be low-diversity. These findings suggest that ITV actively maintains diversity by helping the species on the margins of persistence, but mostly in habitats of relatively low alpha and beta diversity.}, language = {en} } @article{SchlaegelSignerHerdeetal.2019, author = {Schl{\"a}gel, Ulrike E. and Signer, Johannes and Herde, Antje and Eden, Sophie and Jeltsch, Florian and Eccard, Jana and Dammhahn, Melanie}, title = {Estimating interactions between individuals from concurrent animal movements}, series = {Methods in ecology and evolution : an official journal of the British Ecological Society}, volume = {10}, journal = {Methods in ecology and evolution : an official journal of the British Ecological Society}, number = {8}, publisher = {Wiley}, address = {Hoboken}, issn = {2041-210X}, doi = {10.1111/2041-210X.13235}, pages = {1234 -- 1245}, year = {2019}, abstract = {Animal movements arise from complex interactions of individuals with their environment, including both conspecific and heterospecific individuals. Animals may be attracted to each other for mating, social foraging, or information gain, or may keep at a distance from others to avoid aggressive encounters related to, e.g., interference competition, territoriality, or predation. With modern tracking technology, more datasets are emerging that allow to investigate fine-scale interactions between free-ranging individuals from movement data, however, few methods exist to disentangle fine-scale behavioural responses of interacting individuals when these are highly individual-specific. In a framework of step-selection functions, we related movements decisions of individuals to dynamic occurrence distributions of other individuals obtained through kriging of their movement paths. Using simulated data, we tested the method's ability to identify various combinations of attraction, avoidance, and neutrality between individuals, including asymmetric (i.e. non-mutual) behaviours. Additionally, we analysed radio-telemetry data from concurrently tracked small rodents (bank vole, Myodes glareolus) to test whether our method could detect biologically plausible behaviours. We found that our method was able to successfully detect and distinguish between fine-scale interactions (attraction, avoidance, neutrality), even when these were asymmetric between individuals. The method worked best when confounding factors were taken into account in the step-selection function. However, even when failing to do so (e.g. due to missing information), interactions could be reasonably identified. In bank voles, responses depended strongly on the sexes of the involved individuals and matched expectations. Our approach can be combined with conventional uses of step-selection functions to tease apart the various drivers of movement, e.g. the influence of the physical and the social environment. In addition, the method is particularly useful in studying interactions when responses are highly individual-specific, i.e. vary between and towards different individuals, making our method suitable for both single-species and multi-species analyses (e.g. in the context of predation or competition).}, language = {en} } @article{NathanMonkArlinghausetal.2022, author = {Nathan, Ran and Monk, Christopher T. and Arlinghaus, Robert and Adam, Timo and Al{\´o}s, Josep and Assaf, Michael and Baktoft, Henrik and Beardsworth, Christine E. and Bertram, Michael G. and Bijleveld, Allert and Brodin, Tomas and Brooks, Jill L. and Campos-Candela, Andrea and Cooke, Steven J. and Gjelland, Karl O. and Gupte, Pratik R. and Harel, Roi and Hellstrom, Gustav and Jeltsch, Florian and Killen, Shaun S. and Klefoth, Thomas and Langrock, Roland and Lennox, Robert J. and Lourie, Emmanuel and Madden, Joah R. and Orchan, Yotam and Pauwels, Ine S. and Riha, Milan and R{\"o}leke, Manuel and Schl{\"a}gel, Ulrike and Shohami, David and Signer, Johannes and Toledo, Sivan and Vilk, Ohad and Westrelin, Samuel and Whiteside, Mark A. and Jaric, Ivan}, title = {Big-data approaches lead to an increased understanding of the ecology of animal movement}, series = {Science}, volume = {375}, journal = {Science}, number = {6582}, publisher = {American Assoc. for the Advancement of Science}, address = {Washington}, issn = {0036-8075}, doi = {10.1126/science.abg1780}, pages = {734 -- +}, year = {2022}, abstract = {Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.}, language = {en} }