35166
2013
2013
eng
93
101
9
2
14
article
Elsevier
Jena
1
--
--
--
How can we bring together empiricists and modellers in functional biodiversity research?
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.
Basic and applied ecology : Journal of the Gesellschaft für Ökologie
10.1016/j.baae.2013.01.001
1439-1791
wos:2011-2013
WOS:000317352100001
Jeltsch, F (reprint author), Univ Potsdam, Dept Plant Ecol & Nat Conservat, Maulbeerallee 2, D-14469 Potsdam, Germany., jeltsch@uni-potsdam.de
Deutsche Forschungsgemeinschaft DFG; DFG Priority Programme 1374
"Infrastructure-Biodiversity-Exploratories" [JE 207/5-1]
Florian Jeltsch
Niels Blaum
Ulrich Brose
Joseph D. Chipperfield
Yann Clough
Nina Farwig
Katja Geissler
Catherine H. Graham
Volker Grimm
Thomas Hickler
Andreas Huth
Felix May
Katrin M. Meyer
Jörn Pagel
Björn Reineking
Matthias C. Rillig
Katriona Shea
Frank Martin Schurr
Boris Schroeder
Katja Tielbörger
Lina Weiss
Kerstin Wiegand
Thorsten Wiegand
Christian Wirth
Damaris Zurell
eng
uncontrolled
Biodiversity theory
eng
uncontrolled
Biodiversity experiments
eng
uncontrolled
Conservation management
eng
uncontrolled
Decision-making
eng
uncontrolled
Ecosystem functions and services
eng
uncontrolled
Forecasting
eng
uncontrolled
Functional traits
eng
uncontrolled
Global change
eng
uncontrolled
Monitoring programmes
eng
uncontrolled
Interdisciplinarity
Institut für Geowissenschaften
Referiert
Institut für Erd- und Umweltwissenschaften
53476
2017
2017
eng
761
773
13
2
106
article
Wiley
Hoboken
1
2017-06-09
2017-06-09
--
Neighbourhood effects on plant reproduction
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.
The journal of ecology
an experimental-analytical framework and its application to the invasive Senecio inaequidens
10.1111/1365-2745.12816
0022-0477
1365-2745
wos:2018
WOS:000425046300025
Lachmuth, S (reprint author), Martin Luther Univ Halle Wittenberg, Plant Ecol, Halle, Germany.; Lachmuth, S (reprint author), German Ctr Integrat Biodivers Res iDiv, Leipzig, Germany., susanne.lachmuth@botanik.uni-halle.de
Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [SCHU 2259/3-1]
2022-01-17T15:22:56+00:00
sword
importub
filename=package.tar
719a539a6a27832a8ed53227b7a8763d
false
true
Susanne Lachmuth
Colette Henrichmann
Juliane Horn
Jörn Pagel
Frank M. Schurr
eng
uncontrolled
Allee effect
eng
uncontrolled
biological invasion
eng
uncontrolled
competition
eng
uncontrolled
density dependence
eng
uncontrolled
facilitation
eng
uncontrolled
plant-plant interactions
eng
uncontrolled
pollination
eng
uncontrolled
reproductive success
eng
uncontrolled
spatially explicit model
eng
uncontrolled
trait-based neighbourhood model
Geowissenschaften
Institut für Geowissenschaften
Referiert
Import
35496
2012
2012
eng
2225
2239
15
12
39
article
WILEY-BLACKWELL
HOBOKEN
1
--
--
--
Parameter and uncertainty estimation for process-oriented population and
distribution models: data, statistics and the niche
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.
JOURNAL OF BIOGEOGRAPHY
10.1111/j.1365-2699.2012.02772.x
0305-0270
1365-2699
wos:2011-2013
WOS:000311384100014
Marion, G (reprint author), Biomath & Stat Scotland, James Clerk Maxwell Bldg,Kings Bldg, Edinburgh EH9 3JZ, Midlothian, Scotland.
, glenn@bioss.ac.uk
LOEWE initiative for scientific and economic excellence of the German
federal state of Hesse; Scottish Government’s RESAS; ERC [233066];
Biodiversity and Climate Research Centre (BiK-F), part of the LOEWE
programme ’Landes-Offensive zur Entwicklung
Wissenschaftlich-okonomischer Exzellenz’ of Hesse’s Ministry of Higher
Education, Research and the Arts
Glenn Marion
Greg J. McInerny
Jörn Pagel
Stephen Catterall
Alex R. Cook
Florian Hartig
O&rsquo
Robert B. Hara
eng
uncontrolled
Bayesian inference
eng
uncontrolled
demography
eng
uncontrolled
dispersal
eng
uncontrolled
dynamic models
eng
uncontrolled
dynamic range models
eng
uncontrolled
establishment
eng
uncontrolled
global change
eng
uncontrolled
niche models
eng
uncontrolled
species distribution models
7803
2014
eng
VII, 147
doctoralthesis
1
2015-06-25
--
--
Statistical process-based models for the understanding and prediction of range dynamics
online registration
Potsdam, Univ., Diss., 2014
Jörn Pagel
Biowissenschaften; Biologie
Institut für Biochemie und Biologie
Universität Potsdam
37663
2014
2014
eng
751
760
10
8
5
article
Wiley-Blackwell
Hoboken
1
--
--
--
Quantifying range-wide variation in population trends from local abundance surveys and widespread opportunistic occurrence records
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.
Methods in ecology and evolution : an official journal of the British Ecological Society
10.1111/2041-210X.12221
2041-210X
2041-2096
wos:2014
WOS:000340600400004
Pagel, J (reprint author), Univ Potsdam, D-14469 Potsdam, Germany., jpagel@uni-potsdam.de
Countryside Council for Wales, Defra; Joint Nature Conservation
Committee; Forestry Commission; Natural England; Natural Environment
Research Council; Scottish Natural Heritage; University of Potsdam
Graduate Initiative on Ecological Modelling (UPGradE); German Federal
Agency for Nature Conservation [FKZ 806 82 270 - K1]; German Research
Foundation [SCHU 2259/3-1, SCHU 2259/5-1]; Biodiversity and Climate
Ministry of Higher Education, Research and the Arts, Germany
Jörn Pagel
Barbara J. Anderson
Wolfgang Cramer
Richard Fox
Florian Jeltsch
David B. Roy
Chris D. Thomas
Frank Martin Schurr
eng
uncontrolled
atlas data
eng
uncontrolled
Bayesian statistics
eng
uncontrolled
biogeography
eng
uncontrolled
butterflies
eng
uncontrolled
citizen science programme
eng
uncontrolled
conservation biology
eng
uncontrolled
count data
eng
uncontrolled
macroecology
eng
uncontrolled
state-space model
Institut für Biochemie und Biologie
Referiert
11274
2008
2008
eng
article
1
--
--
--
Annual plants under cyclic disturbance regime : better understanding through model aggregation
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.
1960 = DOI: 10.1890/07-1305.1
1051-0761
allegro:1991-2014
10106313
Ecological applications. - ISSN 1051-0761. - 18 (2008), 8, S. 2000 - 2015
Jörn Pagel
Katrin Fritzsch
Robert Biedermann
Boris Schröder-Esselbach
Institut für Biochemie und Biologie
Referiert
36183
2012
2012
eng
293
304
12
2
21
article
Wiley-Blackwell
Malden
1
--
--
--
Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics
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.
Global ecology and biogeography : a journal of macroecology
10.1111/j.1466-8238.2011.00663.x
1466-822X
wos:2011-2013
WOS:000298912900018
Pagel, J (reprint author), Univ Potsdam, Inst Biochem & Biol, Maulbeerallee 2, D-14469 Potsdam, Germany., jpagel@uni-potsdam.de
University of Potsdam Graduate Initiative on Ecological Modelling
(UPGradE); German Federal Agency for Nature Conservation; European
Commission [GOCE-CT-2003-506675, 226701]; European Union
[MTKD-CT-2006-042261]
Jörn Pagel
Frank Martin Schurr
eng
uncontrolled
Biogeography
eng
uncontrolled
ecological forecasts
eng
uncontrolled
global change
eng
uncontrolled
hierarchical Bayesian statistics
eng
uncontrolled
long-distance dispersal
eng
uncontrolled
niche theory
eng
uncontrolled
process-based model
eng
uncontrolled
range shifts
eng
uncontrolled
spatial demography
eng
uncontrolled
species distribution modelling
Institut für Biochemie und Biologie
Referiert
35494
2012
2012
eng
2146
2162
17
12
39
review
Wiley-Blackwell
Hoboken
1
--
--
--
How to understand species' niches and range dynamics: a demographic research agenda for biogeography
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.
Journal of biogeography
10.1111/j.1365-2699.2012.02737.x
0305-0270
wos:2011-2013
WOS:000311384100008
Schurr, FM (reprint author), Univ Montpellier 2, Inst Sci Evolut, UMR 5554, F-34095 Montpellier 05, France., frank.schurr@univ-montp2.fr
German Research Foundation DFG [SCHU 2259/3-1, SCHU 2259/5-1,
SCHR1000/6-1]; European Research Council ERC [233066]; Villum Kann
Rasmussen Foundation [VKR09b-141]; Danish Council for Independent
Research - Natural Sciences [11-106163]; Biodiversity and Climate
Research Centre (BiK-F), part of the LOEWE programme 'Landes-Offensive
zur Entwicklung Wissenschaftlich-okonomischer Exzellenz' of Hesse's
Ministry of Higher Education, Research and the Arts
Frank Martin Schurr
Jörn Pagel
Juliano Sarmento Sarmento
Juergen Groeneveld
Olga Bykova
Robert B. O'Hara
Florian Hartig
W. Daniel Kissling
H. Peter Linder
Guy F. Midgley
Boris Schröder-Esselbach
Alexander Singer
Niklaus E. Zimmermann
eng
uncontrolled
Biodiversity monitoring
eng
uncontrolled
climate change
eng
uncontrolled
ecological forecasts
eng
uncontrolled
ecological niche modelling
eng
uncontrolled
ecological theory
eng
uncontrolled
geographical range shifts
eng
uncontrolled
global environmental change
eng
uncontrolled
mechanistic models
eng
uncontrolled
migration
eng
uncontrolled
process-based statistics
Institut für Geowissenschaften
Referiert
Institut für Erd- und Umweltwissenschaften
37334
2014
2014
eng
1198
1209
12
12
37
article
Wiley-Blackwell
Hoboken
1
--
--
--
The influence of interspecific interactions on species range expansion rates
Ecography : pattern and diversity in ecology ; research papers forum
10.1111/j.1600-0587.2013.00574.x
0906-7590
1600-0587
wos:2014
WOS:000345849400005
Svenning, JC (reprint author), Aarhus Univ, Dept Biosci, Ny Munkegade 114, DK-8000 Aarhus C, Denmark., svenning@biology.au.dk
Danish Council for Independent ResearchNatural Sciences [10-085056];
Aarhus Univ.; Aarhus Univ. Research Foundation; European Research
Council [ERC-2012-StG-310886-HISTFUNC]; Natural Science and Engineering
Council of Canada; European Research Council under European Community
[281422]; EraNet BiodivERsA project [ANR-11-EBID-002 CONNECT]; Austrian
Panel for Climate Research (SPEC-Adapt) [B175127]; Swiss National
Science Foundation (SNF) [315230-122434]; German Research Foundation
(DFG) [SCHU 2259/3-1, SCHU 2259/5-1]; Univ. of Florida Foundation
Jens-Christian Svenning
Dominique Gravel
Robert D. Holt
Frank Martin Schurr
Wilfried Thuiller
Tamara Muenkemueller
Katja H. Schiffers
Stefan Dullinger
Thomas C. Edwards
Thomas Hickler
Steven I. Higgins
Julia E. M. S. Nabel
Jörn Pagel
Signe Normand
Institut für Biochemie und Biologie
Referiert
32272
2010
2010
eng
article
1
--
--
--
The virtual ecologist approach : simulating data and observers
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.
http://www3.interscience.wiley.com/cgi-bin/issn?DESCRIPTOR=PRINTISSN&VALUE=0030-1299
10.1111/j.1600-0706.2009.18284.x
0030-1299
allegro:1991-2014
10107741
Oikos. - ISSN 0030-1299. - 119 (2010), 4, S. 622 - 635
Damaris Zurell
Uta Berger
Juliano Sarmento Cabral
Florian Jeltsch
Christine N. Meynard
Tamara Muenkemueller
Nana Nehrbass
Jörn Pagel
Bjoern Reineking
Boris Schroeder
Volker Grimm
Institut für Geowissenschaften
Referiert
Institut für Erd- und Umweltwissenschaften