@article{LachmuthHenrichmannHornetal.2017, author = {Lachmuth, Susanne and Henrichmann, Colette and Horn, Juliane and Pagel, J{\"o}rn and Schurr, Frank M.}, title = {Neighbourhood effects on plant reproduction}, series = {The journal of ecology}, volume = {106}, journal = {The journal of ecology}, number = {2}, publisher = {Wiley}, address = {Hoboken}, issn = {0022-0477}, doi = {10.1111/1365-2745.12816}, pages = {761 -- 773}, year = {2017}, abstract = {Density dependence is of fundamental importance for population and range dynamics. Density-dependent reproduction of plants arises from competitive and facilitative plant-plant interactions that can be pollination independent or pollination mediated. In small and sparse populations, conspecific density dependence often turns from negative to positive and causes Allee effects. Reproduction may also increase with heterospecific density (community-level Allee effect), but the underlying mechanisms are poorly understood and the consequences for community dynamics can be complex. Allee effects have crucial consequences for the conservation of declining species, but also the dynamics of range edge populations. In invasive species, Allee effects may slow or stop range expansion. Observational studies in natural plant communities cannot distinguish whether reproduction is limited by pollination-mediated interactions among plants or by other neighbourhood effects (e.g. competition for abiotic resources). Even experimental pollen supply cannot distinguish whether variation in reproduction is caused by direct density effects or by plant traits correlated with density. Finally, it is unknown over which spatial scales pollination-mediated interactions occur. To circumvent these problems, we introduce a comprehensive experimental and analytical framework which simultaneously (1) manipulates pollen availability and quality by hand pollination and pollinator exclusion, (2) manipulates neighbourhoods by transplanting target plants, and (3) analyses the effects of con- and heterospecific neighbourhoods on reproduction with spatially explicit trait-based neighbourhood models. Synthesis. By manipulating both pollen availability and target plant locations within neighbourhoods, we can comprehensively analyse spatially explicit density dependence of plant reproduction. This experimental approach enhances our ability to understand the dynamics of sparse populations and of species geographical ranges.}, language = {en} } @article{SvenningGravelHoltetal.2014, author = {Svenning, Jens-Christian and Gravel, Dominique and Holt, Robert D. and Schurr, Frank Martin and Thuiller, Wilfried and Muenkemueller, Tamara and Schiffers, Katja H. and Dullinger, Stefan and Edwards, Thomas C. and Hickler, Thomas and Higgins, Steven I. and Nabel, Julia E. M. S. and Pagel, J{\"o}rn and Normand, Signe}, title = {The influence of interspecific interactions on species range expansion rates}, 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/j.1600-0587.2013.00574.x}, pages = {1198 -- 1209}, year = {2014}, language = {en} } @article{PagelAndersonCrameretal.2014, author = {Pagel, J{\"o}rn and Anderson, Barbara J. and Cramer, Wolfgang and Fox, Richard and Jeltsch, Florian and Roy, David B. and Thomas, Chris D. and Schurr, Frank Martin}, title = {Quantifying range-wide variation in population trends from local abundance surveys and widespread opportunistic occurrence records}, series = {Methods in ecology and evolution : an official journal of the British Ecological Society}, volume = {5}, journal = {Methods in ecology and evolution : an official journal of the British Ecological Society}, number = {8}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {2041-210X}, doi = {10.1111/2041-210X.12221}, pages = {751 -- 760}, year = {2014}, abstract = {2. We present a hierarchical model that integrates observations from multiple sources to estimate spatio-temporal abundance trends. The model links annual population densities on a spatial grid to both long-term count data and to opportunistic occurrence records from a citizen science programme. Specific observation models for both data types explicitly account for differences in data structure and quality. 3. We test this novel method in a virtual study with simulated data and apply it to the estimation of abundance dynamics across the range of a butterfly species (Pyronia tithonus) in Great Britain between 1985 and 2004. The application to simulated and real data demonstrates how the hierarchical model structure accommodates various sources of uncertainty which occur at different stages of the link between observational data and the modelled abundance, thereby it accounts for these uncertainties in the inference of abundance variations. 4. We show that by using hierarchical observation models that integrate different types of commonly available data sources, we can improve the estimates of variation in species abundances across space and time. This will improve our ability to detect regional trends and can also enhance the empirical basis for understanding range dynamics.}, language = {en} } @phdthesis{Pagel2014, author = {Pagel, J{\"o}rn}, title = {Statistical process-based models for the understanding and prediction of range dynamics}, pages = {VII, 147}, year = {2014}, language = {en} } @article{JeltschBlaumBroseetal.2013, author = {Jeltsch, Florian and Blaum, Niels and Brose, Ulrich and Chipperfield, Joseph D. and Clough, Yann and Farwig, Nina and Geissler, Katja and Graham, Catherine H. and Grimm, Volker and Hickler, Thomas and Huth, Andreas and May, Felix and Meyer, Katrin M. and Pagel, J{\"o}rn and Reineking, Bj{\"o}rn and Rillig, Matthias C. and Shea, Katriona and Schurr, Frank Martin and Schroeder, Boris and Tielb{\"o}rger, Katja and Weiss, Lina and Wiegand, Kerstin and Wiegand, Thorsten and Wirth, Christian and Zurell, Damaris}, title = {How can we bring together empiricists and modellers in functional biodiversity research?}, series = {Basic and applied ecology : Journal of the Gesellschaft f{\"u}r {\"O}kologie}, volume = {14}, journal = {Basic and applied ecology : Journal of the Gesellschaft f{\"u}r {\"O}kologie}, number = {2}, publisher = {Elsevier}, address = {Jena}, issn = {1439-1791}, doi = {10.1016/j.baae.2013.01.001}, pages = {93 -- 101}, year = {2013}, abstract = {Improving our understanding of biodiversity and ecosystem functioning and our capacity to inform ecosystem management requires an integrated framework for functional biodiversity research (FBR). However, adequate integration among empirical approaches (monitoring and experimental) and modelling has rarely been achieved in FBR. We offer an appraisal of the issues involved and chart a course towards enhanced integration. A major element of this path is the joint orientation towards the continuous refinement of a theoretical framework for FBR that links theory testing and generalization with applied research oriented towards the conservation of biodiversity and ecosystem functioning. We further emphasize existing decision-making frameworks as suitable instruments to practically merge these different aims of FBR and bring them into application. This integrated framework requires joint research planning, and should improve communication and stimulate collaboration between modellers and empiricists, thereby overcoming existing reservations and prejudices. The implementation of this integrative research agenda for FBR requires an adaptation in most national and international funding schemes in order to accommodate such joint teams and their more complex structures and data needs.}, language = {en} } @misc{SchurrPagelSarmentoetal.2012, author = {Schurr, Frank Martin and Pagel, J{\"o}rn and Sarmento, Juliano Sarmento and Groeneveld, Juergen and Bykova, Olga and O'Hara, Robert B. and Hartig, Florian and Kissling, W. Daniel and Linder, H. Peter and Midgley, Guy F. and Schr{\"o}der-Esselbach, Boris and Singer, Alexander and Zimmermann, Niklaus E.}, title = {How to understand species' niches and range dynamics: a demographic research agenda for biogeography}, series = {Journal of biogeography}, volume = {39}, journal = {Journal of biogeography}, number = {12}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0305-0270}, doi = {10.1111/j.1365-2699.2012.02737.x}, pages = {2146 -- 2162}, year = {2012}, abstract = {Range dynamics causes mismatches between a species geographical distribution and the set of suitable environments in which population growth is positive (the Hutchinsonian niche). This is because sourcesink population dynamics cause species to occupy unsuitable environments, and because environmental change creates non-equilibrium situations in which species may be absent from suitable environments (due to migration limitation) or present in unsuitable environments that were previously suitable (due to time-delayed extinction). Because correlative species distribution models do not account for these processes, they are likely to produce biased niche estimates and biased forecasts of future range dynamics. Recently developed dynamic range models (DRMs) overcome this problem: they statistically estimate both range dynamics and the underlying environmental response of demographic rates from species distribution data. This process-based statistical approach qualitatively advances biogeographical analyses. Yet, the application of DRMs to a broad range of species and study systems requires substantial research efforts in statistical modelling, empirical data collection and ecological theory. Here we review current and potential contributions of these fields to a demographic understanding of niches and range dynamics. Our review serves to formulate a demographic research agenda that entails: (1) advances in incorporating process-based models of demographic responses and range dynamics into a statistical framework, (2) systematic collection of data on temporal changes in distribution and abundance and on the response of demographic rates to environmental variation, and (3) improved theoretical understanding of the scaling of demographic rates and the dynamics of spatially coupled populations. This demographic research agenda is challenging but necessary for improved comprehension and quantification of niches and range dynamics. It also forms the basis for understanding how niches and range dynamics are shaped by evolutionary dynamics and biotic interactions. Ultimately, the demographic research agenda should lead to deeper integration of biogeography with empirical and theoretical ecology.}, language = {en} } @article{MarionMcInernyPageletal.2012, author = {Marion, Glenn and McInerny, Greg J. and Pagel, J{\"o}rn and Catterall, Stephen and Cook, Alex R. and Hartig, Florian and O\&rsquo, and Hara, Robert B.}, title = {Parameter and uncertainty estimation for process-oriented population and distribution models: data, statistics and the niche}, series = {JOURNAL OF BIOGEOGRAPHY}, volume = {39}, journal = {JOURNAL OF BIOGEOGRAPHY}, number = {12}, publisher = {WILEY-BLACKWELL}, address = {HOBOKEN}, issn = {0305-0270}, doi = {10.1111/j.1365-2699.2012.02772.x}, pages = {2225 -- 2239}, year = {2012}, abstract = {The spatial distribution of a species is determined by dynamic processes such as reproduction, mortality and dispersal. Conventional static species distribution models (SDMs) do not incorporate these processes explicitly. This limits their applicability, particularly for non-equilibrium situations such as invasions or climate change. In this paper we show how dynamic SDMs can be formulated and fitted to data within a Bayesian framework. Our focus is on discrete state-space Markov process models which provide a flexible framework to account for stochasticity in key demographic processes, including dispersal, growth and competition. We show how to construct likelihood functions for such models (both discrete and continuous time versions) and how these can be combined with suitable observation models to conduct Bayesian parameter inference using computational techniques such as Markov chain Monte Carlo. We illustrate the current state-of-the-art with three contrasting examples using both simulated and empirical data. The use of simulated data allows the robustness of the methods to be tested with respect to deficiencies in both data and model. These examples show how mechanistic understanding of the processes that determine distribution and abundance can be combined with different sources of information at a range of spatial and temporal scales. Application of such techniques will enable more reliable inference and projections, e.g. under future climate change scenarios than is possible with purely correlative approaches. Conversely, confronting such process-oriented niche models with abundance and distribution data will test current understanding and may ultimately feedback to improve underlying ecological theory.}, language = {en} } @article{PagelSchurr2012, author = {Pagel, J{\"o}rn and Schurr, Frank Martin}, title = {Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics}, series = {Global ecology and biogeography : a journal of macroecology}, volume = {21}, journal = {Global ecology and biogeography : a journal of macroecology}, number = {2}, publisher = {Wiley-Blackwell}, address = {Malden}, issn = {1466-822X}, doi = {10.1111/j.1466-8238.2011.00663.x}, pages = {293 -- 304}, year = {2012}, abstract = {Aim The study and prediction of speciesenvironment relationships is currently mainly based on species distribution models. These purely correlative models neglect spatial population dynamics and assume that species distributions are in equilibrium with their environment. This causes biased estimates of species niches and handicaps forecasts of range dynamics under environmental change. Here we aim to develop an approach that statistically estimates process-based models of range dynamics from data on species distributions and permits a more comprehensive quantification of forecast uncertainties. Innovation We present an approach for the statistical estimation of process-based dynamic range models (DRMs) that integrate Hutchinson's niche concept with spatial population dynamics. In a hierarchical Bayesian framework the environmental response of demographic rates, local population dynamics and dispersal are estimated conditional upon each other while accounting for various sources of uncertainty. The method thus: (1) jointly infers species niches and spatiotemporal population dynamics from occurrence and abundance data, and (2) provides fully probabilistic forecasts of future range dynamics under environmental change. In a simulation study, we investigate the performance of DRMs for a variety of scenarios that differ in both ecological dynamics and the data used for model estimation. Main conclusions Our results demonstrate the importance of considering dynamic aspects in the collection and analysis of biodiversity data. In combination with informative data, the presented framework has the potential to markedly improve the quantification of ecological niches, the process-based understanding of range dynamics and the forecasting of species responses to environmental change. It thereby strengthens links between biogeography, population biology and theoretical and applied ecology.}, 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{PagelFritzschBiedermannetal.2008, author = {Pagel, J{\"o}rn and Fritzsch, Katrin and Biedermann, Robert and Schr{\"o}der-Esselbach, Boris}, title = {Annual plants under cyclic disturbance regime : better understanding through model aggregation}, issn = {1051-0761}, year = {2008}, abstract = {In their application for conservation ecology, 'classical' analytical models and individual-based simulation models (IBMs) both entail their specific strengths and weaknesses, either in providing a detailed and realistic representation of processes or in regard to a comprehensive model analysis. This well-known dilemma may be resolved by the combination of both approaches when tackling certain problems of conservation ecology. Following this idea, we present the complementary use of both an IBM and a matrix population model in a case study on grassland conservation management. First, we develop a spatially explicit IBM to simulate the long-term response of the annual plant Thlaspi perfoliatum (Brassicaceae), claspleaf pennycress, to different management schemes (annual mowing vs. infrequent rototilling) based on field experiments. In order to complement the simulation results by further analyses, we aggregate the IBM to a spatially nonexplicit deterministic matrix population model. Within the periodic environment created by management regimes, population dynamics are described by periodic products of annual transition matrices. Such periodic matrix products provide a very conclusive framework to study the responses of species to different management return intervals. Thus, using tools of matrix model analysis (e.g., loop analysis), we can both identify dormancy within the age-structured seed bank as the pivotal strategy for persistence under cyclic disturbance regimes and reveal crucial thresholds in some less certain parameters. Results of matrix model analyses are therefore successfully tested by comparing their results to the respective IBM simulations. Their implications for an enhanced scientific basis for management decisions are discussed as well as some general benefits and limitations of the use of aggregating modeling approaches in conservation.}, language = {en} }