@phdthesis{Cabral2009, author = {Cabral, Juliano Sarmento}, title = {Demographic processes determining the range dynamics of plant species, and their consequences for biodiversity maintenance in the face of environmental change}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-41188}, school = {Universit{\"a}t Potsdam}, year = {2009}, abstract = {The present thesis aims to introduce process-based model for species range dynamics that can be fitted to abundance data. For this purpose, the well-studied Proteaceae species of the South African Cape Floristic Region (CFR) offer a great data set to fit process-based models. These species are subject to wildflower harvesting and environmental threats like habitat loss and climate change. The general introduction of this thesis presents shortly the available models for species distribution modelling. Subsequently, it presents the feasibility of process-based modelling. Finally, it introduces the study system as well as the objectives and layout. In Chapter 1, I present the process-based model for range dynamics and a statistical framework to fit it to abundance distribution data. The model has a spatially-explicit demographic submodel (describing dispersal, reproduction, mortality and local extinction) and an observation submodel (describing imperfect detection of individuals). The demographic submodel links species-specific habitat models describing the suitable habitat and process-based demographic models that consider local dynamics and anemochoric seed dispersal between populations. After testing the fitting framework with simulated data, I applied it to eight Proteaceae species with different demographic properties. Moreover, I assess the role of two other demographic mechanisms: positive (Allee effects) and negative density-dependence. Results indicate that Allee effects and overcompensatory local dynamics (including chaotic behaviour) seem to be important for several species. Most parameter estimates quantitatively agreed with independent data. Hence, the presented approach seemed to suit the demand of investigating non-equilibrium scenarios involving wildflower harvesting (Chapter 2) and environmental change (Chapter 3). The Chapter 2 addresses the impacts of wildflower harvesting. The chapter includes a sensitivity analysis over multiple spatial scales and demographic properties (dispersal ability, strength of Allee effects, maximum reproductive rate, adult mortality, local extinction probability and carrying capacity). Subsequently, harvesting effects are investigated on real case study species. Plant response to harvesting showed abrupt threshold behavior. Species with short-distance seed dispersal, strong Allee effects, low maximum reproductive rate, high mortality and high local extinction are most affected by harvesting. Larger spatial scales benefit species response, but the thresholds become sharper. The three case study species supported very low to moderate harvesting rates. Summarizing, demographic knowledge about the study system and careful identification of the spatial scale of interest should guide harvesting assessments and conservation of exploited species. The sensitivity analysis' results can be used to qualitatively assess harvesting impacts for poorly studied species. I investigated in Chapter 3 the consequences of past habitat loss, future climate change and their interaction on plant response. I use the species-specific estimates of the best model describing local dynamics obtained in Chapter 1. Both habitat loss and climate change had strong negative impacts on species dynamics. Climate change affected mainly range size and range filling due to habitat reductions and shifts combined with low colonization. Habitat loss affected mostly local abundances. The scenario with both habitat loss and climate change was the worst for most species. However, this impact was better than expected by simple summing of separate effects of habitat loss and climate change. This is explained by shifting ranges to areas less affected by humans. Range size response was well predicted by the strength of environmental change, whereas range filling and local abundance responses were better explained by demographic properties. Hence, risk assessments under global change should consider demographic properties. Most surviving populations were restricted to refugia, serving as key conservation focus.The findings obtained for the study system as well as the advantages, limitations and potentials of the model presented here are further discussed in the General Discussion. In summary, the results indicate that 1) process-based demographic models for range dynamics can be fitted to data; 2) demographic processes improve species distribution models; 3) different species are subject to different processes and respond differently to environmental change and exploitation; 4) density regulation type and Allee effects should be considered when investigating range dynamics of species; 5) the consequences of wildflower harvesting, habitat loss and climate change could be disastrous for some species, but impacts vary depending on demographic properties; 6) wildflower harvesting impacts varies over spatial scale; 7) The effects of habitat loss and climate change are not always additive.}, language = {en} } @misc{CabralValenteHartig2017, author = {Cabral, Juliano Sarmento and Valente, Luis and Hartig, Florian}, title = {Mechanistic simulation models in macroecology and biogeography}, series = {Ecography : pattern and diversity in ecology}, volume = {40}, journal = {Ecography : pattern and diversity in ecology}, number = {2}, publisher = {Wiley}, address = {Hoboken}, issn = {0906-7590}, doi = {10.1111/ecog.02480}, pages = {267 -- 280}, year = {2017}, abstract = {Macroecology and biogeography are concerned with understanding biodiversity patterns across space and time. In the past, the two disciplines have addressed this question mainly with correlative approaches, despite frequent calls for more mechanistic explanations. Recent advances in computational power, theoretical understanding, and statistical tools are, however, currently facilitating the development of more system-oriented, mechanistic models. We review these models, identify different model types and theoretical frameworks, compare their processes and properties, and summarize emergent findings. We show that ecological (physiology, demographics, dispersal, biotic interactions) and evolutionary processes, as well as environmental and human-induced drivers, are increasingly modelled mechanistically; and that new insights into biodiversity dynamics emerge from these models. Yet, substantial challenges still lie ahead for this young research field. Among these, we identify scaling, calibration, validation, and balancing complexity as pressing issues. Moreover, particular process combinations are still understudied, and so far models tend to be developed for specific applications. Future work should aim at developing more flexible and modular models that not only allow different ecological theories to be expressed and contrasted, but which are also built for tight integration with all macroecological data sources. Moving the field towards such a 'systems macroecology' will test and improve our understanding of the causal pathways through which eco-evolutionary processes create diversity patterns across spatial and temporal scales.}, 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} } @misc{DormannSchymanskiCabraletal.2012, author = {Dormann, Carsten F. and Schymanski, Stanislaus J. and Cabral, Juliano Sarmento and Chuine, Isabelle and Graham, Catherine and Hartig, Florian and Kearney, Michael and Morin, Xavier and R{\"o}mermann, Christine and Schr{\"o}der-Esselbach, Boris and Singer, Alexander}, title = {Correlation and process in species distribution models: bridging a dichotomy}, series = {Journal of biogeography}, volume = {39}, journal = {Journal of biogeography}, number = {12}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0305-0270}, doi = {10.1111/j.1365-2699.2011.02659.x}, pages = {2119 -- 2131}, year = {2012}, abstract = {Within the field of species distribution modelling an apparent dichotomy exists between process-based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlativeprocess spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the processcorrelation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process-based approaches to species distribution modelling lags far behind more correlative (process-implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process-explicit species distribution models and how they may complement current approaches to study species distributions.}, language = {en} } @misc{BorregaardAmorimBorgesetal.2017, author = {Borregaard, Michael K. and Amorim, Isabel R. and Borges, Paulo A. V. and Cabral, Juliano Sarmento and Fernandez-Palacios, Jose M. and Field, Richard and Heaney, Lawrence R. and Kreft, Holger and Matthews, Thomas J. and Olesen, Jens M. and Price, Jonathan and Rigal, Francois and Steinbauer, Manuel J. and Triantis, Konstantinos A. and Valente, Luis and Weigelt, Patrick and Whittaker, Robert J.}, title = {Oceanic island biogeography through the lens of the general dynamic model: assessment and prospect}, series = {Biological reviews}, volume = {92}, journal = {Biological reviews}, publisher = {Wiley}, address = {Hoboken}, issn = {1464-7931}, doi = {10.1111/brv.12256}, pages = {830 -- 853}, year = {2017}, language = {en} } @article{RadchukDeLaenderCabraletal.2019, author = {Radchuk, Viktoriia and De Laender, Frederik and Cabral, Juliano Sarmento and Boulangeat, Isabelle and Crawford, Michael Scott and Bohn, Friedrich and De Raedt, Jonathan and Scherer, Cedric and Svenning, Jens-Christian and Thonicke, Kirsten and Schurr, Frank M. and Grimm, Volker and Kramer-Schadt, Stephanie}, title = {The dimensionality of stability depends on disturbance type}, series = {Ecology letters}, volume = {22}, journal = {Ecology letters}, number = {4}, publisher = {Wiley}, address = {Hoboken}, issn = {1461-023X}, doi = {10.1111/ele.13226}, pages = {674 -- 684}, year = {2019}, abstract = {Ecosystems respond in various ways to disturbances. Quantifying ecological stability therefore requires inspecting multiple stability properties, such as resistance, recovery, persistence and invariability. Correlations among these properties can reduce the dimensionality of stability, simplifying the study of environmental effects on ecosystems. A key question is how the kind of disturbance affects these correlations. We here investigated the effect of three disturbance types (random, species-specific, local) applied at four intensity levels, on the dimensionality of stability at the population and community level. We used previously parameterized models that represent five natural communities, varying in species richness and the number of trophic levels. We found that disturbance type but not intensity affected the dimensionality of stability and only at the population level. The dimensionality of stability also varied greatly among species and communities. Therefore, studying stability cannot be simplified to using a single metric and multi-dimensional assessments are still to be recommended.}, language = {en} } @misc{KalinkatCabralDarwalletal.2017, author = {Kalinkat, Gregor and Cabral, Juliano Sarmento and Darwall, William and Ficetola, G. Francesco and Fisher, Judith L. and Giling, Darren P. and Gosselin, Marie-Pierre and Grossart, Hans-Peter and Jaehnig, Sonja C. and Jeschke, Jonathan M. and Knopf, Klaus and Larsen, Stefano and Onandia, Gabriela and Paetzig, Marlene and Saul, Wolf-Christian and Singer, Gabriel and Sperfeld, Erik and Jaric, Ivan}, title = {Flagship umbrella species needed for the conservation of overlooked aquatic biodiversity}, series = {Conservation biology : the journal of the Society for Conservation Biology}, volume = {31}, journal = {Conservation biology : the journal of the Society for Conservation Biology}, publisher = {Wiley}, address = {Hoboken}, issn = {0888-8892}, doi = {10.1111/cobi.12813}, pages = {481 -- 485}, year = {2017}, language = {en} }