@article{deOliveiraSilvaPiratelliZurelletal.2021,
  author    = {de Oliveira-Silva, Anna Elizabeth and Piratelli, Augusto Jo{\~a}o and Zurell, Damaris and da Silva, Fernando Rodrigues},
  title     = {Vegetation cover restricts habitat suitability predictions of endemic Brazilian Atlantic Forest birds},
  series = {Perspectives in Ecology and Conservation},
  volume    = {20},
  journal   = {Perspectives in Ecology and Conservation},
  number    = {1},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {2530-0644},
  doi       = {10.1016/j.pecon.2021.09.002},
  pages     = {1 -- 8},
  year      = {2021},
  abstract  = {Ecological niche models (ENMs) are often used to investigate how climatic variables from known occurrence records can estimate potential species range distribution. Although climate-based ENMs provide critical baseline information, the inclusion of non-climatic predictors related to vegetation cover might generate more realistic scenarios. This assumption is particularly relevant for species with life-history traits related to forest habitats and sensitive to habitat loss and fragmentation. Here, we developed ENMs for 36 Atlantic Forest endemic birds considering two sets of predictor variables: (i) climatic variables only and (ii) climatic variables combined with the percentage of remaining native vegetation. We hypothesized that the inclusion of native vegetation data would decrease the potential range distribution of forest-dependent species by limiting their occurrence in regions harboring small areas of native vegetation habitats, despite otherwise favorable climatic conditions. We also expected that habitat restriction in the climate-vegetation models would be more pronounced for highly forest-dependent birds. The inclusion of vegetation data in the modeling procedures restricted the final distribution ranges of 22 out of 36 modeled species, while the 14 remaining presented an expansion of their ranges. We observed that species with high and medium forest dependency showed higher restriction in range size predictions between predictor sets than species with low forest dependency, which showed no alteration or range expansion. Overall, our results suggest that ENMs based on climatic and landscape variables may be a useful tool for conservationists to better understand the dynamic of bird species distributions in threatened and highly fragmented regions such as the Atlantic Forest hotspot.(c) 2021 Associacao Brasileira de Cie circumflex accent ncia Ecol ogica e Conservacao. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ ).},
  language  = {en}
}
@article{ZurellGrimmRossmanithetal.2012,
  author    = {Zurell, Damaris and Grimm, Volker and Rossmanith, Eva and Zbinden, Niklaus and Zimmermann, Niklaus E. and Schr{\"o}der-Esselbach, Boris},
  title     = {Uncertainty in predictions of range dynamics black grouse climbing the Swiss Alps},
  series = {Ecography : pattern and diversity in ecology ; research papers forum},
  volume    = {35},
  journal   = {Ecography : pattern and diversity in ecology ; research papers forum},
  number    = {7},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {0906-7590},
  doi       = {10.1111/j.1600-0587.2011.07200.x},
  pages     = {590 -- 603},
  year      = {2012},
  abstract  = {Empirical species distribution models (SDMs) constitute often the tool of choice for the assessment of rapid climate change effects on species vulnerability. Conclusions regarding extinction risks might be misleading, however, because SDMs do not explicitly incorporate dispersal or other demographic processes. Here, we supplement SDMs with a dynamic population model 1) to predict climate-induced range dynamics for black grouse in Switzerland, 2) to compare direct and indirect measures of extinction risks, and 3) to quantify uncertainty in predictions as well as the sources of that uncertainty. To this end, we linked models of habitat suitability to a spatially explicit, individual-based model. In an extensive sensitivity analysis, we quantified uncertainty in various model outputs introduced by different SDM algorithms, by different climate scenarios and by demographic model parameters. Potentially suitable habitats were predicted to shift uphill and eastwards. By the end of the 21st century, abrupt habitat losses were predicted in the western Prealps for some climate scenarios. In contrast, population size and occupied area were primarily controlled by currently negative population growth and gradually declined from the beginning of the century across all climate scenarios and SDM algorithms. However, predictions of population dynamic features were highly variable across simulations. Results indicate that inferring extinction probabilities simply from the quantity of suitable habitat may underestimate extinction risks because this may ignore important interactions between life history traits and available habitat. Also, in dynamic range predictions uncertainty in SDM algorithms and climate scenarios can become secondary to uncertainty in dynamic model components. Our study emphasises the need for principal evaluation tools like sensitivity analysis in order to assess uncertainty and robustness in dynamic range predictions. A more direct benefit of such robustness analysis is an improved mechanistic understanding of dynamic species responses to climate change.},
  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{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}
}
@article{ZurellJeltschDormannetal.2009,
  author    = {Zurell, Damaris and Jeltsch, Florian and Dormann, Carsten F. and Schr{\"o}der-Esselbach, Boris},
  title     = {Static species distribution models in dynamically changing systems : how good can predictions really be?},
  issn      = {0906-7590},
  doi       = {10.1111/j.1600-0587.2009.05810.x},
  year      = {2009},
  abstract  = {SDM performance varied for different range dynamics. Prediction accuracies decreased when abrupt range shifts occurred as species were outpaced by the rate of climate change, and increased again when a new equilibrium situation was realised. When ranges contracted, prediction accuracies increased as the absences were predicted well. Far- dispersing species were faster in tracking climate change, and were predicted more accurately by SDMs than short- dispersing species. BRTs mostly outperformed GLMs. The presence of a predator, and the inclusion of its incidence as an environmental predictor, made BRTs and GLMs perform similarly. Results are discussed in light of other studies dealing with effects of ecological traits and processes on SDM performance. Perspectives are given on further advancements of SDMs and for possible interfaces with more mechanistic approaches in order to improve predictions under environmental change.},
  language  = {en}
}
@article{ZurellKoenigMalchowetal.2022,
  author    = {Zurell, Damaris and K{\"o}nig, Christian and Malchow, Anne-Kathleen and Kapitza, Simon and Bocedi, Greta and Travis, Justin M. J. and Fandos, Guillermo},
  title     = {Spatially explicit models for decision-making in animal conservation and restoration},
  series = {Ecography : pattern and diversity in ecology / Nordic Ecologic Society Oikos},
  journal   = {Ecography : pattern and diversity in ecology / Nordic Ecologic Society Oikos},
  number    = {4},
  publisher = {Wiley-Blackwell},
  address   = {Oxford},
  issn      = {1600-0587},
  doi       = {10.1111/ecog.05787},
  pages     = {1 -- 16},
  year      = {2022},
  abstract  = {Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision-making in conservation and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and to identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79\%), towards the species and population level (80\%) and towards conservation (rather than restoration) applications (71\%). Correlative niche models were the most widely used model type. Dynamic models as well as the gene-to-individual level and the community-to-ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10\% of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration and decision-making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by 1) developing a toolbox with multiple, easier-to-use methods, 2) improving calibration and validation of dynamic modelling approaches and 3) developing best-practise guidelines for applying these models. Further, more robust decision-making can be achieved by 4) combining multiple modelling approaches to assess uncertainty, and 5) placing models at the core of adaptive management. These efforts must be accompanied by long-term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.},
  language  = {en}
}
@misc{ZurellKoenigMalchowetal.2021,
  author    = {Zurell, Damaris and K{\"o}nig, Christian and Malchow, Anne-Kathleen and Kapitza, Simon and Bocedi, Greta and Travis, Justin M. J. and Fandos, Guillermo},
  title     = {Spatially explicit models for decision-making in animal conservation and restoration},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  volume    = {2022},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  edition   = {4},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-54991},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-549915},
  pages     = {1 -- 16},
  year      = {2021},
  abstract  = {Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision-making in conservation and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and to identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79\%), towards the species and population level (80\%) and towards conservation (rather than restoration) applications (71\%). Correlative niche models were the most widely used model type. Dynamic models as well as the gene-to-individual level and the community-to-ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10\% of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration and decision-making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by 1) developing a toolbox with multiple, easier-to-use methods, 2) improving calibration and validation of dynamic modelling approaches and 3) developing best-practise guidelines for applying these models. Further, more robust decision-making can be achieved by 4) combining multiple modelling approaches to assess uncertainty, and 5) placing models at the core of adaptive management. These efforts must be accompanied by long-term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.},
  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{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 = {Ecography},
  volume    = {44},
  journal   = {Ecography},
  number    = {10},
  publisher = {John Wiley \& Sons, Inc.},
  address   = {New Jersey},
  issn      = {1600-0587},
  pages     = {10},
  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}
}
@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}
}