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 - THES A1 - Malchow, Anne-Kathleen T1 - Developing an integrated platform for predicting niche and range dynamics BT - inverse calibration of spatially-explicit eco-evolutionary models N2 - Species are adapted to the environment they live in. Today, most environments are subjected to rapid global changes induced by human activity, most prominently land cover and climate changes. Such transformations can cause adjustments or disruptions in various eco-evolutionary processes. The repercussions of this can appear at the population level as shifted ranges and altered abundance patterns. This is where global change effects on species are usually detected first. To understand how eco-evolutionary processes act and interact to generate patterns of range and abundance and how these processes themselves are influenced by environmental conditions, spatially-explicit models provide effective tools. They estimate a species’ niche as the set of environmental conditions in which it can persist. However, the currently most commonly used models rely on static correlative associations that are established between a set of spatial predictors and observed species distributions. For this, they assume stationary conditions and are therefore unsuitable in contexts of global change. Better equipped are process-based models that explicitly implement algorithmic representations of eco-evolutionary mechanisms and evaluate their joint dynamics. These models have long been regarded as difficult to parameterise, but an increased data availability and improved methods for data integration lessen this challenge. Hence, the goal of this thesis is to further develop process-based models, integrate them into a complete modelling workflow, and provide the tools and guidance for their successful application. With my thesis, I presented an integrated platform for spatially-explicit eco-evolutionary modelling and provided a workflow for their inverse calibration to observational data. In the first chapter, I introduced RangeShiftR, a software tool that implements an individual-based modelling platform for the statistical programming language R. Its open-source licensing, extensive help pages and available tutorials make it accessible to a wide audience. In the second chapter, I demonstrated a comprehensive workflow for the specification, calibration and validation of RangeShiftR by the example of the red kite in Switzerland. The integration of heterogeneous data sources, such as literature and monitoring data, allowed to successfully calibrate the model. It was then used to make validated, spatio-temporal predictions of future red kite abundance. The presented workflow can be adopted to any study species if data is available. In the third chapter, I extended RangeShiftR to directly link demographic processes to climatic predictors. This allowed me to explore the climate-change responses of eight Swiss breeding birds in more detail. Specifically, the model could identify the most influential climatic predictors, delineate areas of projected demographic suitability, and attribute current population trends to contemporary climate change. My work shows that the application of complex, process-based models in conservation-relevant contexts is feasible, utilising available tools and data. Such models can be successfully calibrated and outperform other currently used modelling approaches in terms of predictive accuracy. Their projections can be used to predict future abundances or to assess alternative conservation scenarios. They further improve our mechanistic understanding of niche and range dynamics under climate change. However, only fully mechanistic models, that include all relevant processes, allow to precisely disentangle the effects of single processes on observed abundances. In this respect, the RangeShiftR model still has potential for further extensions that implement missing influential processes, such as species interactions. Dynamic, process-based models are needed to adequately model a dynamic reality. My work contributes towards the advancement, integration and dissemination of such models. This will facilitate numeric, model-based approaches for species assessments, generate ecological insights and strengthen the reliability of predictions on large spatial scales under changing conditions. N2 - Arten sind an ihren jeweiligen Lebensraum angepasst, doch viele Lebensräume sind heute einem globalen Wandel unterworfen. Dieser äußert sich vor allem in Veränderungen von Landnutzung und Klima, welche durch menschliche Aktivitäten verursacht werden und ganze Ökosysteme in ihrem Gefüge stören können. Störungen der grundlegenden öko-evolutionären Prozesse können auf der Populationsebene in Form von veränderten Verbreitungsgebieten und Häufigkeitsmustern sichtbar werden. Hier werden die Auswirkungen des globalen Wandels auf eine Art oftmals zuerst beobachtet. Um zu untersuchen, wie die Wirkung und Wechselwirkung der verschiedenen öko-evolutionären Prozesse die beobachteten Verbreitungs- und Häufigkeitsmuster erzeugen, und wie diese Prozesse wiederum von Umweltbedingungen beeinflusst werden, stellen räumlich explizite Modelle wirksame Instrumente dar. Sie beschreiben die ökologische Nische einer Art, also die Gesamtheit aller Umweltbedingungen, unter denen die Art fortbestehen kann. Die derzeit am häufigsten verwendeten Modelle stützen sich auf statische, korrelative Zusammenhänge, die zwischen bestimmten räumlichen Prädiktoren und den beobachteten Artverteilungen hergestellt werden. Allerdings werden dabei stationäre Bedingungen angenommen, was sie im Kontext des globalen Wandels ungeeignet macht. Deutlich besser geeignet sind prozessbasierte Modelle, welche explizite, algorithmische Repräsentationen von ökologischen Prozessen beinhalten und deren gemeinsame Dynamik berechnen. Solche Modelle galten lange Zeit als schwierig zu parametrisieren, doch die zunehmende Verfügbarkeit von Beobachtungsdaten sowie die verbesserten Methoden zur Datenintegration machen ihre Verwendung zunehmend praktikabel. Das Ziel der vorliegenden Arbeit ist es, diese prozessbasierten Modelle weiterzuentwickeln, sie in umfassende Modellierungsabläufe einzubinden, sowie Software und Anleitungen für ihre erfolgreiche Anwendung verfügbar zu machen. In meiner Dissertation präsentiere ich eine integrierte Plattform für räumlich-explizite, öko-evolutionäre Modellierung und entwickle einen Arbeitsablauf für dessen inverse Kalibrierung an Beobachtungsdaten. Im ersten Kapitel stelle ich RangeShiftR vor: eine Software, die eine individuenbasierte Modellierungsplattform für die statistische Programmiersprache R implementiert. Durch die Open-Source-Lizenzierung, umfangreichen Hilfeseiten und online verfügbaren Tutorials ist RangeShiftR einem breiten Publikum zugänglich. Im zweiten Kapitel demonstriere ich einen vollständigen Modellierungsablauf am Beispiel des Rotmilans in der Schweiz, der die Spezifikation, Kalibrierung und Validierung von RangeShiftR umfasst.Durch die Integration heterogener Datenquellen, wie Literatur- und Monitoringdaten, konnte das Modell erfolgreich kalibriert werden. Damit konnten anschließend validierte, raum-zeitliche Vorhersagen über das Vorkommen des Rotmilans erstellt und die dafür relevanten Prozesse identifiziert werden. Der vorgestellte Arbeitsablauf kann auf andere Arten übertragen werden, sofern geeignete Daten verfügbar sind. Im dritten Kapitel habe ich RangeShiftR erweitert, sodass demografische Prozessraten direkt mit Klimavariablen verknüpft werden können. Dies ermöglichte es, die Reaktionen von acht Schweizer Brutvogelarten auf den Klimawandel genauer zu untersuchen. Insbesondere konnte das Modell die einflussreichsten klimatischen Faktoren identifizieren, demografisch geeignete Gebiete abgrenzen und aktuelle Populationstrends auf den bisherigen Klimawandel zurückführen. Meine Arbeit zeigt, dass die Anwendung komplexer, prozessbasierter Modelle in naturschutzrelevanten Kontexten mit verfügbaren Daten möglich ist. Solche Modelle können erfolgreich kalibriert werden und andere, derzeit verwendete Modellierungsansätze in Bezug auf ihre Vorhersagegenauigkeit übertreffen. Ihre Projektionen können zur Vorhersage zukünftiger Artvorkommen und zur Einschätzung alternativer Naturschutzmaßnahmen verwendet werden. Sie verbessern außerdem unser mechanistisches Verständnis von Nischen- und Verbreitungsdynamiken unter dem Einfluss des Klimawandels. Jedoch ermöglichen nur vollständig prozessbasierte Modelle, die alle relevanten Prozesse vereinen, eine korrekte Aufschlüsselung der Auswirkungen einzelner Prozesse auf die beobachteten Abundanzen. In dieser Hinsicht hat das RangeShiftR-Modell noch Potenzial für Weiterentwicklungen, um fehlende, einflussreiche Prozesse hinzuzufügen, wie zum Beispiel die Interaktionen zwischen Arten. Um eine dynamische Realität adäquat abbilden zu können, werden dynamische, prozessbasierte Modelle benötigt. Meine Arbeit leistet einen Beitrag zur Weiterentwicklung, Integration und Verbreitung solcher Modelle und stärkt somit die Anwendung numerischer, modellbasierter Methoden für die Bewertung des Zustands von Arten, die Untersuchung ökologischer Zusammenhänge und die Steigerung der Zuverlässigkeit von Vorhersagen auf großen räumlichen Skalen unter Umweltveränderungen. T2 - Entwicklung einer integrierten Modellierungs-Plattform zur Vorhersage von Nischen- und Verbreitungs-dynamiken: Inverse Kalibrierung räumlich-expliziter öko-evolutionärer Modelle KW - species distribution modelling KW - Bayesian inference KW - individual-based modelling KW - range shifts KW - ecological modelling KW - population dynamics KW - Bayes'sche Inferenz KW - ökologische Modellierung KW - individuen-basierte Modellierung KW - Populationsdynamik KW - Arealverschiebungen KW - Artverbreitungsmodelle Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-602737 ER - TY - JOUR A1 - Kramer-Schadt, Stephanie A1 - Niedballa, Jürgen A1 - Pilgrim, John D. A1 - Schröder-Esselbach, Boris A1 - Lindenborn, Jana A1 - Reinfelder, Vanessa A1 - Stillfried, Milena A1 - Heckmann, Ilja A1 - Scharf, Anne K. A1 - Augeri, Dave M. A1 - Cheyne, Susan M. A1 - Hearn, Andrew J. A1 - Ross, Joanna A1 - Macdonald, David W. A1 - Mathai, John A1 - Eaton, James A1 - Marshall, Andrew J. A1 - Semiadi, Gono A1 - Rustam, Rustam A1 - Bernard, Henry A1 - Alfred, Raymond A1 - Samejima, Hiromitsu A1 - Duckworth, J. W. A1 - Breitenmoser-Wuersten, Christine A1 - Belant, Jerrold L. A1 - Hofer, Heribert A1 - Wilting, Andreas T1 - The importance of correcting for sampling bias in MaxEnt species distribution models JF - Diversity & distributions : a journal of biological invasions and biodiversity N2 - AimAdvancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better-surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo. LocationBorneo, Southeast Asia. MethodsWe collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range-restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north-eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas. ResultsSpatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased. Main ConclusionsWe conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning. KW - Borneo KW - carnivora KW - conservation planning KW - ecological niche modelling KW - maximum entropy (MaxEnt) KW - sampling bias KW - Southeast Asia KW - species distribution modelling KW - viverridae Y1 - 2013 U6 - https://doi.org/10.1111/ddi.12096 SN - 1366-9516 SN - 1472-4642 VL - 19 IS - 11 SP - 1366 EP - 1379 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Dellinger, Agnes S. A1 - Essl, Franz A1 - Hojsgaard, Diego A1 - Kirchheimer, Bernhard A1 - Klatt, Simone A1 - Dawson, Wayne A1 - Pergl, Jan A1 - Pysek, Petr A1 - van Kleunen, Mark A1 - Weber, Ewald A1 - Winter, Marten A1 - Hoerandl, Elvira A1 - Dullinger, Stefan T1 - Niche dynamics of alien species do not differ among sexual and apomictic flowering plants JF - New phytologist : international journal of plant science N2 - We compiled global occurrence data sets of 13 congeneric sexual and apomictic species pairs, and used principal components analysis (PCA) and kernel smoothers to compare changes in climatic niche optima, breadths and unfilling/expansion between native and alien ranges. Niche change metrics were compared between sexual and apomictic species. All 26 species showed changes in niche optima and/or breadth and 14 species significantly expanded their climatic niches. However, we found no effect of the reproductive system on niche dynamics. Instead, species with narrower native niches showed higher rates of niche expansion in the alien ranges. Our results suggest that niche shifts are frequent in plant invasions but evolutionary potential may not be of major importance for such shifts. Niche dynamics rather appear to be driven by changes of the realized niche without adaptive change of the fundamental climatic niche. KW - adaptation KW - asexual reproduction KW - niche shifts KW - plant invasion KW - reproductive system KW - species distribution modelling Y1 - 2016 U6 - https://doi.org/10.1111/nph.13694 SN - 0028-646X SN - 1469-8137 VL - 209 SP - 1313 EP - 1323 PB - Wiley-Blackwell CY - Hoboken ER -