@article{AllanManningAltetal.2015, author = {Allan, Eric and Manning, Pete and Alt, Fabian and Binkenstein, Julia and Blaser, Stefan and Bl{\"u}thgen, Nico and B{\"o}hm, Stefan and Grassein, Fabrice and H{\"o}lzel, Norbert and Klaus, Valentin H. and Kleinebecker, Till and Morris, E. Kathryn and Oelmann, Yvonne and Prati, Daniel and Renner, Swen C. and Rillig, Matthias C. and Schaefer, Martin and Schloter, Michael and Schmitt, Barbara and Sch{\"o}ning, Ingo and Schrumpf, Marion and Solly, Emily and Sorkau, Elisabeth and Steckel, Juliane and Steffen-Dewenter, Ingolf and Stempfhuber, Barbara and Tschapka, Marco and Weiner, Christiane N. and Weisser, Wolfgang W. and Werner, Michael and Westphal, Catrin and Wilcke, Wolfgang and Fischer, Markus}, title = {Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition}, series = {Ecology letters}, volume = {18}, journal = {Ecology letters}, number = {8}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {1461-023X}, doi = {10.1111/ele.12469}, pages = {834 -- 843}, year = {2015}, abstract = {Global change, especially land-use intensification, affects human well-being by impacting the delivery of multiple ecosystem services (multifunctionality). However, whether biodiversity loss is a major component of global change effects on multifunctionality in real-world ecosystems, as in experimental ones, remains unclear. Therefore, we assessed biodiversity, functional composition and 14 ecosystem services on 150 agricultural grasslands differing in land-use intensity. We also introduce five multifunctionality measures in which ecosystem services were weighted according to realistic land-use objectives. We found that indirect land-use effects, i.e. those mediated by biodiversity loss and by changes to functional composition, were as strong as direct effects on average. Their strength varied with land-use objectives and regional context. Biodiversity loss explained indirect effects in a region of intermediate productivity and was most damaging when land-use objectives favoured supporting and cultural services. In contrast, functional composition shifts, towards fast-growing plant species, strongly increased provisioning services in more inherently unproductive grasslands.}, language = {en} } @article{BailleulGrimmChionetal.2013, author = {Bailleul, Frederic and Grimm, Volker and Chion, Clement and Hammill, Mike}, title = {Modeling implications of food resource aggregation on animal migration phenology}, series = {Ecology and evolution}, volume = {3}, journal = {Ecology and evolution}, number = {8}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {2045-7758}, doi = {10.1002/ece3.656}, pages = {2535 -- 2546}, year = {2013}, abstract = {The distribution of poikilotherms is determined by the thermal structure of the marine environment that they are exposed to. Recent research has indicated that changes in migration phenology of beluga whales in the Arctic are triggered by changes in the thermal structure of the marine environment in their summering area. If sea temperatures reflect the spatial distribution of food resources, then changes in the thermal regime will affect how homogeneous or clumped food is distributed. We explore, by individual-based modelling, the hypothesis that changes in migration phenology are not necessarily or exclusively triggered by changes in food abundance, but also by changes in the spatial aggregation of food. We found that the level of food aggregation can significantly affect the relationship between the timing of the start of migration to the winter grounds and the total prey capture of individuals. Our approach strongly indicates that changes in the spatial distribution of food resources should be considered for understanding and quantitatively predicting changes in the phenology of animal migration.}, language = {en} } @article{BinzerGuillRalletal.2016, author = {Binzer, Amrei and Guill, Christian and Rall, Bj{\"o}rn C. and Brose, Ulrich}, title = {Interactive effects of warming, eutrophication and size structure: impacts on biodiversity and food-web structure}, series = {Global change biology}, volume = {22}, journal = {Global change biology}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {1354-1013}, doi = {10.1111/gcb.13086}, pages = {220 -- 227}, year = {2016}, abstract = {Warming and eutrophication are two of the most important global change stressors for natural ecosystems, but their interaction is poorly understood. We used a dynamic model of complex, size-structured food webs to assess interactive effects on diversity and network structure. We found antagonistic impacts: Warming increases diversity in eutrophic systems and decreases it in oligotrophic systems. These effects interact with the community size structure: Communities of similarly sized species such as parasitoid-host systems are stabilized by warming and destabilized by eutrophication, whereas the diversity of size-structured predator-prey networks decreases strongly with warming, but decreases only weakly with eutrophication. Nonrandom extinction risks for generalists and specialists lead to higher connectance in networks without size structure and lower connectance in size-structured communities. Overall, our results unravel interactive impacts of warming and eutrophication and suggest that size structure may serve as an important proxy for predicting the community sensitivity to these global change stressors.}, language = {en} } @article{HegerBernardVerdierGessleretal.2019, author = {Heger, Tina and Bernard-Verdier, Maud and Gessler, Arthur and Greenwood, Alex D. and Grossart, Hans-Peter and Hilker, Monika and Keinath, Silvia and Kowarik, Ingo and K{\"u}ffer, Christoph and Marquard, Elisabeth and Mueller, Johannes and Niemeier, Stephanie and Onandia, Gabriela and Petermann, Jana S. and Rillig, Matthias C. and Rodel, Mark-Oliver and Saul, Wolf-Christian and Schittko, Conrad and Tockner, Klement and Joshi, Jasmin Radha and Jeschke, Jonathan M.}, title = {Towards an Integrative, Eco-Evolutionary Understanding of Ecological Novelty: Studying and Communicating Interlinked Effects of Global Change}, series = {Bioscience}, volume = {69}, journal = {Bioscience}, number = {11}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {0006-3568}, doi = {10.1093/biosci/biz095}, pages = {888 -- 899}, year = {2019}, abstract = {Global change has complex eco-evolutionary consequences for organisms and ecosystems, but related concepts (e.g., novel ecosystems) do not cover their full range. Here we propose an umbrella concept of "ecological novelty" comprising (1) a site-specific and (2) an organism-centered, eco-evolutionary perspective. Under this umbrella, complementary options for studying and communicating effects of global change on organisms, ecosystems, and landscapes can be included in a toolbox. This allows researchers to address ecological novelty from different perspectives, e.g., by defining it based on (a) categorical or continuous measures, (b) reference conditions related to sites or organisms, and (c) types of human activities. We suggest striving for a descriptive, non-normative usage of the term "ecological novelty" in science. Normative evaluations and decisions about conservation policies or management are important, but require additional societal processes and engagement with multiple stakeholders.}, language = {en} } @misc{KisslingDormannGroeneveldetal.2012, author = {Kissling, W. D. and Dormann, Carsten F. and Groeneveld, Juergen and Hickler, Thomas and K{\"u}hn, Ingolf and McInerny, Greg J. and Montoya, Jose M. and R{\"o}mermann, Christine and Schiffers, Katja and Schurr, Frank Martin and Singer, Alexander and Svenning, Jens-Christian and Zimmermann, Niklaus E. and O'Hara, Robert B.}, title = {Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents}, series = {Journal of biogeography}, volume = {39}, journal = {Journal of biogeography}, number = {12}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0305-0270}, doi = {10.1111/j.1365-2699.2011.02663.x}, pages = {2163 -- 2178}, year = {2012}, abstract = {Aim Biotic interactions within guilds or across trophic levels have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of species interaction distribution models (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co-occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non-stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio-temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co-occurrence datasets across large-scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio-temporal data on biotic interactions in multispecies communities.}, language = {en} } @article{KoussoroplisPincebourdeWacker2017, author = {Koussoroplis, Apostolos-Manuel and Pincebourde, Sylvain and Wacker, Alexander}, title = {Understanding and predicting physiological performance of organisms in fluctuating and multifactorial environments}, series = {Ecological monographs : a publication of the Ecological Society of America.}, volume = {87}, journal = {Ecological monographs : a publication of the Ecological Society of America.}, publisher = {Wiley}, address = {Hoboken}, issn = {0012-9615}, doi = {10.1002/ecm.1247}, pages = {178 -- 197}, year = {2017}, abstract = {Understanding how variance in environmental factors affects physiological performance, population growth, and persistence is central in ecology. Despite recent interest in the effects of variance in single biological drivers, such as temperature, we have lacked a comprehensive framework for predicting how the variances and covariances between multiple environmental factors will affect physiological rates. Here, we integrate current theory on variance effects with co-limitation theory into a single unified conceptual framework that has general applicability. We show how the framework can be applied (1) to generate mathematically tractable predictions of the physiological effects of multiple fluctuating co-limiting factors, (2) to understand how each co-limiting factor contributes to these effects, and (3) to detect mechanisms such as acclimation or physiological stress when they are at play. We show that the statistical covariance of co-limiting factors, which has not been considered before, can be a strong driver of physiological performance in various ecological contexts. Our framework can provide powerful insights on how the global change-induced shifts in multiple environmental factors affect the physiological performance of organisms.}, language = {en} } @article{KuerschnerSchererRadchuketal.2021, author = {K{\"u}rschner, Tobias and Scherer, C{\´e}dric and Radchuk, Viktoriia and Blaum, Niels and Kramer-Schadt, Stephanie}, title = {Movement can mediate temporal mismatches between resource availability and biological events in host-pathogen interactions}, series = {Ecology and evolution}, volume = {11}, journal = {Ecology and evolution}, number = {10}, publisher = {Wiley}, address = {Hoboken}, issn = {2045-7758}, doi = {10.1002/ece3.7478}, pages = {5728 -- 5741}, year = {2021}, abstract = {Global change is shifting the timing of biological events, leading to temporal mismatches between biological events and resource availability. These temporal mismatches can threaten species' populations. Importantly, temporal mismatches not only exert strong pressures on the population dynamics of the focal species, but can also lead to substantial changes in pairwise species interactions such as host-pathogen systems. We adapted an established individual-based model of host-pathogen dynamics. The model describes a viral agent in a social host, while accounting for the host's explicit movement decisions. We aimed to investigate how temporal mismatches between seasonal resource availability and host life-history events affect host-pathogen coexistence, that is, disease persistence. Seasonal resource fluctuations only increased coexistence probability when in synchrony with the hosts' biological events. However, a temporal mismatch reduced host-pathogen coexistence, but only marginally. In tandem with an increasing temporal mismatch, our model showed a shift in the spatial distribution of infected hosts. It shifted from an even distribution under synchronous conditions toward the formation of disease hotspots, when host life history and resource availability mismatched completely. The spatial restriction of infected hosts to small hotspots in the landscape initially suggested a lower coexistence probability due to the critical loss of susceptible host individuals within those hotspots. However, the surrounding landscape facilitated demographic rescue through habitat-dependent movement. Our work demonstrates that the negative effects of temporal mismatches between host resource availability and host life history on host-pathogen coexistence can be reduced through the formation of temporary disease hotspots and host movement decisions, with implications for disease management under disturbances and global change.}, 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{MooijTrolleJeppesenetal.2010, author = {Mooij, Wolf M. and Trolle, Dennis and Jeppesen, Erik and Arhonditsis, George B. and Belolipetsky, Pavel V. and Chitamwebwa, Deonatus B. R. and Degermendzhy, Andrey G. and DeAngelis, Donald L. and Domis, Lisette Nicole de Senerpont and Downing, Andrea S. and Elliott, J. Alex and Fragoso Jr, Carlos Ruberto and Gaedke, Ursula and Genova, Svetlana N. and Gulati, Ramesh D. and H{\aa}kanson, Lars and Hamilton, David P. and Hipsey, Matthew R. and 't Hoen, Jochem and H{\"u}lsmann, Stephan and Los, F. Hans and Makler-Pick, Vardit and Petzoldt, Thomas and Prokopkin, Igor G. and Rinke, Karsten and Schep, Sebastiaan A. and Tominaga, Koji and Van Dam, Anne A. and Van Nes, Egbert H. and Wells, Scott A. and Janse, Jan H.}, title = {Challenges and opportunities for integrating lake ecosystem modelling approaches}, series = {Aquatic ecology}, volume = {44}, journal = {Aquatic ecology}, publisher = {Springer Science + Business Media B.V.}, address = {Dordrecht}, issn = {1573-5125}, doi = {10.1007/s10452-010-9339-3}, pages = {633 -- 667}, year = {2010}, abstract = {A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.}, 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} }