@misc{MalchowBocediPalmeretal.2021, author = {Malchow, Anne-Kathleen and Bocedi, Greta and Palmer, Stephen C. F. and Travis, Justin M. J. and Zurell, Damaris}, title = {RangeShiftR: an R package for individual-based simulation of spatial eco-evolutionary dynamics and speciesu0027 responses to environmental changes}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {10}, issn = {1866-8372}, doi = {10.25932/publishup-52397}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-523979}, pages = {12}, year = {2021}, abstract = {Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species' distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses to environmental change. We present RangeShiftR, an R implementation of a flexible individual-based modelling platform which simulates eco-evolutionary dynamics in a spatially explicit way. The package provides flexible and fast simulations by making the software RangeShifter available for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package's functionality, describe the underlying model structure with its main components and present a short example. RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. As the core code is implemented in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible. The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.}, language = {en} } @article{MalchowBocediPalmeretal.2021, author = {Malchow, Anne-Kathleen and Bocedi, Greta and Palmer, Stephen C. F. and Travis, Justin M. J. and Zurell, Damaris}, title = {RangeShiftR}, series = {Ecography : pattern and diversity in ecology / Nordic Ecologic Society Oikos}, volume = {44}, journal = {Ecography : pattern and diversity in ecology / Nordic Ecologic Society Oikos}, number = {10}, publisher = {Wiley-Blackwell}, address = {Oxford [u.a.]}, issn = {1600-0587}, doi = {10.1111/ecog.05689}, pages = {1443 -- 1452}, year = {2021}, abstract = {Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species' distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses to environmental change. We present RangeShiftR, an R implementation of a flexible individual-based modelling platform which simulates eco-evolutionary dynamics in a spatially explicit way. The package provides flexible and fast simulations by making the software RangeShifter available for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package's functionality, describe the underlying model structure with its main components and present a short example. RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. As the core code is implemented in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible. The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.}, language = {en} } @article{RoticsKaatzResheffetal.2016, author = {Rotics, Shay and Kaatz, Michael and Resheff, Yehezkel S. and Turjeman, Sondra Feldman and Zurell, Damaris and Sapir, Nir and Eggers, Ute and Flack, Andrea and Fiedler, Wolfgang and Jeltsch, Florian and Wikelski, Martin and Nathan, Ran}, title = {The challenges of the first migration: movement and behaviour of juvenile vs. adult white storks with insights regarding juvenile mortality}, series = {Journal of animal ecology : a journal of the British Ecological Society}, volume = {85}, journal = {Journal of animal ecology : a journal of the British Ecological Society}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0021-8790}, doi = {10.1111/1365-2656.12525}, pages = {938 -- 947}, year = {2016}, abstract = {1. Migration conveys an immense challenge, especially for juvenile birds coping with enduring and risky journeys shortly after fledging. Accordingly, juveniles exhibit considerably lower survival rates compared to adults, particularly during migration. Juvenile white storks (Ciconia ciconia), which are known to rely on adults during their first fall migration presumably for navigational purposes, also display much lower annual survival than adults. 2. Using detailed GPS and body acceleration data, we examined the patterns and potential causes of age-related differences in fall migration properties of white storks by comparing first-year juveniles and adults. We compared juvenile and adult parameters of movement, behaviour and energy expenditure (estimated from overall dynamic body acceleration) and placed this in the context of the juveniles' lower survival rate. 3. Juveniles used flapping flight vs. soaring flight 23\% more than adults and were estimated to expend 14\% more energy during flight. Juveniles did not compensate for their higher flight costs by increased refuelling or resting during migration. When juveniles and adults migrated together in the same flock, the juvenile flew mostly behind the adult and was left behind when they separated. Juveniles showed greater improvement in flight efficiency throughout migration compared to adults which appears crucial because juveniles exhibiting higher flight costs suffered increased mortality. 4. Our findings demonstrate the conflict between the juveniles' inferior flight skills and their urge to keep up with mixed adult-juvenile flocks. We suggest that increased flight costs are an important proximate cause of juvenile mortality in white storks and likely in other soaring migrants and that natural selection is operating on juvenile variation in flight efficiency.}, language = {en} } @inproceedings{SapirRoticsKaatzetal.2013, author = {Sapir, N. and Rotics, S. and Kaatz, M. and Davidson, S. and Zurell, Damaris and Eggers, U. and Jeltsch, Florian and Nathan, R. and Wikelski, M.}, title = {Multi-year tracking of white storks (Ciconia ciconia) how the environment shapes the movement and behavior of a soaring-gliding inter-continental migrant}, series = {Integrative and comparative biology}, volume = {53}, booktitle = {Integrative and comparative biology}, number = {3}, publisher = {Oxford Univ. Press}, address = {Cary}, issn = {1540-7063}, pages = {E189 -- E189}, year = {2013}, language = {en} } @article{SchaeferMenzJeltschetal.2017, author = {Sch{\"a}fer, Merlin and Menz, Stephan and Jeltsch, Florian and Zurell, Damaris}, title = {sOAR: a tool for modelling optimal animal life-history strategies in cyclic environments}, series = {Ecography : pattern and diversity in ecology ; research papers forum}, volume = {41}, journal = {Ecography : pattern and diversity in ecology ; research papers forum}, number = {3}, publisher = {Wiley}, address = {Hoboken}, issn = {0906-7590}, doi = {10.1111/ecog.03328}, pages = {551 -- 557}, year = {2017}, abstract = {Periodic environments determine the life cycle of many animals across the globe and the timing of important life history events, such as reproduction and migration. These adaptive behavioural strategies are complex and can only be fully understood (and predicted) within the framework of natural selection in which species adopt evolutionary stable strategies. We present sOAR, a powerful and user-friendly implementation of the well-established framework of optimal annual routine modelling. It allows determining optimal animal life history strategies under cyclic environmental conditions using stochastic dynamic programming. It further includes the simulation of population dynamics under the optimal strategy. sOAR provides an important tool for theoretical studies on the behavioural and evolutionary ecology of animals. It is especially suited for studying bird migration. In particular, we integrated options to differentiate between costs of active and passive flight into the optimal annual routine modelling framework, as well as options to consider periodic wind conditions affecting flight energetics. We provide an illustrative example of sOAR where food supply in the wintering habitat of migratory birds significantly alters the optimal timing of migration. sOAR helps improving our understanding of how complex behaviours evolve and how behavioural decisions are constrained by internal and external factors experienced by the animal. Such knowledge is crucial for anticipating potential species' response to global environmental change.}, language = {en} } @article{ThuillerMuenkemuellerSchiffersetal.2014, author = {Thuiller, Wilfried and Muenkemueller, Tamara and Schiffers, Katja H. and Georges, Damien and Dullinger, Stefan and Eckhart, Vincent M. and Edwards, Thomas C. and Gravel, Dominique and Kunstler, Georges and Merow, Cory and Moore, Kara and Piedallu, Christian and Vissault, Steve and Zimmermann, Niklaus E. and Zurell, Damaris and Schurr, Frank Martin}, title = {Does probability of occurrence relate to population dynamics?}, series = {Ecography : pattern and diversity in ecology ; research papers forum}, volume = {37}, journal = {Ecography : pattern and diversity in ecology ; research papers forum}, number = {12}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0906-7590}, doi = {10.1111/ecog.00836}, pages = {1155 -- 1166}, year = {2014}, abstract = {Interestingly, relationships between demographic parameters and occurrence probability did not vary substantially across degrees of shade tolerance and regions. Although they were influenced by the uncertainty in the estimation of the demographic parameters, we found that r was generally negatively correlated with P-occ, while N, and for most regions K, was generally positively correlated with P-occ. Thus, in temperate forest trees the regions of highest occurrence probability are those with high densities but slow intrinsic population growth rates. The uncertain relationships between demography and occurrence probability suggests caution when linking species distribution and demographic models.}, language = {en} } @phdthesis{Zurell2011, author = {Zurell, Damaris}, title = {Integrating dynamic and statistical modelling approaches in order to improve predictions for scenarios of environmental change}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-56845}, school = {Universit{\"a}t Potsdam}, year = {2011}, abstract = {Species respond to environmental change by dynamically adjusting their geographical ranges. Robust predictions of these changes are prerequisites to inform dynamic and sustainable conservation strategies. Correlative species distribution models (SDMs) relate species' occurrence records to prevailing environmental factors to describe the environmental niche. They have been widely applied in global change context as they have comparably low data requirements and allow for rapid assessments of potential future species' distributions. However, due to their static nature, transient responses to environmental change are essentially ignored in SDMs. Furthermore, neither dispersal nor demographic processes and biotic interactions are explicitly incorporated. Therefore, it has often been suggested to link statistical and mechanistic modelling approaches in order to make more realistic predictions of species' distributions for scenarios of environmental change. In this thesis, I present two different ways of such linkage. (i) Mechanistic modelling can act as virtual playground for testing statistical models and allows extensive exploration of specific questions. I promote this 'virtual ecologist' approach as a powerful evaluation framework for testing sampling protocols, analyses and modelling tools. Also, I employ such an approach to systematically assess the effects of transient dynamics and ecological properties and processes on the prediction accuracy of SDMs for climate change projections. That way, relevant mechanisms are identified that shape the species' response to altered environmental conditions and which should hence be considered when trying to project species' distribution through time. (ii) I supplement SDM projections of potential future habitat for black grouse in Switzerland with an individual-based population model. By explicitly considering complex interactions between habitat availability and demographic processes, this allows for a more direct assessment of expected population response to environmental change and associated extinction risks. However, predictions were highly variable across simulations emphasising the need for principal evaluation tools like sensitivity analysis to assess uncertainty and robustness in dynamic range predictions. Furthermore, I identify data coverage of the environmental niche as a likely cause for contrasted range predictions between SDM algorithms. SDMs may fail to make reliable predictions for truncated and edge niches, meaning that portions of the niche are not represented in the data or niche edges coincide with data limits. Overall, my thesis contributes to an improved understanding of uncertainty factors in predictions of range dynamics and presents ways how to deal with these. Finally I provide preliminary guidelines for predictive modelling of dynamic species' response to environmental change, identify key challenges for future research and discuss emerging developments.}, language = {en} } @article{ZurellBergerCabraletal.2010, author = {Zurell, Damaris and Berger, Uta and Cabral, Juliano Sarmento and Jeltsch, Florian and Meynard, Christine N. and Muenkemueller, Tamara and Nehrbass, Nana and Pagel, J{\"o}rn and Reineking, Bjoern and Schroeder, Boris and Grimm, Volker}, title = {The virtual ecologist approach : simulating data and observers}, issn = {0030-1299}, doi = {10.1111/j.1600-0706.2009.18284.x}, year = {2010}, abstract = {Ecologists carry a well-stocked toolbox with a great variety of sampling methods, statistical analyses and modelling tools, and new methods are constantly appearing. Evaluation and optimisation of these methods is crucial to guide methodological choices. Simulating error-free data or taking high-quality data to qualify methods is common practice. Here, we emphasise the methodology of the 'virtual ecologist' (VE) approach where simulated data and observer models are used to mimic real species and how they are 'virtually' observed. This virtual data is then subjected to statistical analyses and modelling, and the results are evaluated against the 'true' simulated data. The VE approach is an intuitive and powerful evaluation framework that allows a quality assessment of sampling protocols, analyses and modelling tools. It works under controlled conditions as well as under consideration of confounding factors such as animal movement and biased observer behaviour. In this review, we promote the approach as a rigorous research tool, and demonstrate its capabilities and practical relevance. We explore past uses of VE in different ecological research fields, where it mainly has been used to test and improve sampling regimes as well as for testing and comparing models, for example species distribution models. We discuss its benefits as well as potential limitations, and provide some practical considerations for designing VE studies. Finally, research fields are identified for which the approach could be useful in the future. We conclude that VE could foster the integration of theoretical and empirical work and stimulate work that goes far beyond sampling methods, leading to new questions, theories, and better mechanistic understanding of ecological systems.}, language = {en} } @article{ZurellEggersKaatzetal.2015, author = {Zurell, Damaris and Eggers, Ute and Kaatz, Michael and Rotics, Shay and Sapir, Nir and Wikelski, Martin and Nathan, Ran and Jeltsch, Florian}, title = {Individual-based modelling of resource competition to predict density-dependent population dynamics: a case study with white storks}, series = {Oikos}, volume = {124}, journal = {Oikos}, number = {3}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0030-1299}, doi = {10.1111/oik.01294}, pages = {319 -- 330}, year = {2015}, abstract = {Density regulation influences population dynamics through its effects on demographic rates and consequently constitutes a key mechanism explaining the response of organisms to environmental changes. Yet, it is difficult to establish the exact form of density dependence from empirical data. Here, we developed an individual-based model to explore how resource limitation and behavioural processes determine the spatial structure of white stork Ciconia ciconia populations and regulate reproductive rates. We found that the form of density dependence differed considerably between landscapes with the same overall resource availability and between home range selection strategies, highlighting the importance of fine-scale resource distribution in interaction with behaviour. In accordance with theories of density dependence, breeding output generally decreased with density but this effect was highly variable and strongly affected by optimal foraging strategy, resource detection probability and colonial behaviour. Moreover, our results uncovered an overlooked consequence of density dependence by showing that high early nestling mortality in storks, assumed to be the outcome of harsh weather, may actually result from density dependent effects on food provision. Our findings emphasize that accounting for interactive effects of individual behaviour and local environmental factors is crucial for understanding density-dependent processes within spatially structured populations. Enhanced understanding of the ways animal populations are regulated in general, and how habitat conditions and behaviour may dictate spatial population structure and demographic rates is critically needed for predicting the dynamics of populations, communities and ecosystems under changing environmental conditions.}, language = {en} } @misc{ZurellElithSchroederEsselbach2012, author = {Zurell, Damaris and Elith, Jane and Schr{\"o}der-Esselbach, Boris}, title = {Predicting to new environments tools for visualizing model behaviour and impacts on mapped distributions}, series = {Diversity \& distributions : a journal of biological invasions and biodiversity}, volume = {18}, journal = {Diversity \& distributions : a journal of biological invasions and biodiversity}, number = {6}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {1366-9516}, doi = {10.1111/j.1472-4642.2012.00887.x}, pages = {628 -- 634}, year = {2012}, abstract = {Data limitations can lead to unrealistic fits of predictive species distribution models (SDMs) and spurious extrapolation to novel environments. Here, we want to draw attention to novel combinations of environmental predictors that are within the sampled range of individual predictors but are nevertheless outside the sample space. These tend to be overlooked when visualizing model behaviour. They may be a cause of differing model transferability and environmental change predictions between methods, a problem described in some studies but generally not well understood. We here use a simple simulated data example to illustrate the problem and provide new and complementary visualization techniques to explore model behaviour and predictions to novel environments. We then apply these in a more complex real-world example. Our results underscore the necessity of scrutinizing model fits, ecological theory and environmental novelty.}, language = {en} }