TY - JOUR A1 - Pagel, Jörn A1 - Fritzsch, Katrin A1 - Biedermann, Robert A1 - Schröder-Esselbach, Boris T1 - Annual plants under cyclic disturbance regime : better understanding through model aggregation N2 - 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. Y1 - 2008 UR - 1960 = DOI: 10.1890/07-1305.1 SN - 1051-0761 ER - TY - JOUR A1 - Svenning, Jens-Christian A1 - Gravel, Dominique A1 - Holt, Robert D. A1 - Schurr, Frank Martin A1 - Thuiller, Wilfried A1 - Muenkemueller, Tamara A1 - Schiffers, Katja H. A1 - Dullinger, Stefan A1 - Edwards, Thomas C. A1 - Hickler, Thomas A1 - Higgins, Steven I. A1 - Nabel, Julia E. M. S. A1 - Pagel, Jörn A1 - Normand, Signe T1 - The influence of interspecific interactions on species range expansion rates JF - Ecography : pattern and diversity in ecology ; research papers forum Y1 - 2014 U6 - https://doi.org/10.1111/j.1600-0587.2013.00574.x SN - 0906-7590 SN - 1600-0587 VL - 37 IS - 12 SP - 1198 EP - 1209 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Pagel, Jörn A1 - Schurr, Frank Martin T1 - Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics JF - Global ecology and biogeography : a journal of macroecology N2 - 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. KW - Biogeography KW - ecological forecasts KW - global change KW - hierarchical Bayesian statistics KW - long-distance dispersal KW - niche theory KW - process-based model KW - range shifts KW - spatial demography KW - species distribution modelling Y1 - 2012 U6 - https://doi.org/10.1111/j.1466-8238.2011.00663.x SN - 1466-822X VL - 21 IS - 2 SP - 293 EP - 304 PB - Wiley-Blackwell CY - Malden ER - TY - JOUR A1 - Pagel, Jörn A1 - Anderson, Barbara J. A1 - Cramer, Wolfgang A1 - Fox, Richard A1 - Jeltsch, Florian A1 - Roy, David B. A1 - Thomas, Chris D. A1 - Schurr, Frank Martin T1 - Quantifying range-wide variation in population trends from local abundance surveys and widespread opportunistic occurrence records JF - Methods in ecology and evolution : an official journal of the British Ecological Society N2 - 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. KW - atlas data KW - Bayesian statistics KW - biogeography KW - butterflies KW - citizen science programme KW - conservation biology KW - count data KW - macroecology KW - state-space model Y1 - 2014 U6 - https://doi.org/10.1111/2041-210X.12221 SN - 2041-210X SN - 2041-2096 VL - 5 IS - 8 SP - 751 EP - 760 PB - Wiley-Blackwell CY - Hoboken ER -