Fritsch, Matthias

[["dc.bibliographiccitation.firstpage","842"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Global Ecology and Biogeography"],["dc.bibliographiccitation.lastpage","851"],["dc.bibliographiccitation.volume","30"],["dc.contributor.affiliation","Udy, Kristy; 1Agroecology, Department of Crop Sciences University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Fritsch, Matthias; 2Department of Ecosystem Modelling University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Grass, Ingo; 3Ecology of Tropical Agricultural Systems University of Hohenheim Stuttgart Germany"],["dc.contributor.affiliation","Hanß, Sebastian; 2Department of Ecosystem Modelling University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Hartig, Florian; 4Theoretical Ecology University of Regensburg Regensburg Germany"],["dc.contributor.affiliation","Kneib, Thomas; 5Chair of Statistics and Econometrics University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Kreft, Holger; 6Biodiversity, Macroecology and Conservation Biogeography University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Kukunda, Collins B.; 7Forest Inventory and Remote Sensing, Faculty of Forest Sciences and Forest Ecology University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Pe’er, Guy; 9German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Leipzig Germany"],["dc.contributor.affiliation","Reininghaus, Hannah; 1Agroecology, Department of Crop Sciences University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Tietjen, Britta; 11Theoretical Ecology, Institute of Biology Freie Universität Berlin Berlin Germany"],["dc.contributor.affiliation","Tscharntke, Teja; 1Agroecology, Department of Crop Sciences University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","van Waveren, Clara‐Sophie; 2Department of Ecosystem Modelling University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Wiegand, Kerstin; 2Department of Ecosystem Modelling University of Göttingen Göttingen Germany"],["dc.contributor.author","Udy, Kristy"],["dc.contributor.author","Fritsch, Matthias"],["dc.contributor.author","Meyer, Katrin Mareike"],["dc.contributor.author","Grass, Ingo"],["dc.contributor.author","Hanß, Sebastian"],["dc.contributor.author","Hartig, Florian"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Kreft, Holger"],["dc.contributor.author","Kukunda, Collins B."],["dc.contributor.author","Pe’er, Guy"],["dc.contributor.author","Reininghaus, Hannah"],["dc.contributor.author","Tietjen, Britta"],["dc.contributor.author","Tscharntke, Teja"],["dc.contributor.author","Wiegand, Kerstin"],["dc.contributor.author","van Waveren, Clara‐Sophie"],["dc.contributor.editor","Keil, Petr"],["dc.date.accessioned","2021-03-16T11:08:51Z"],["dc.date.available","2021-03-16T11:08:51Z"],["dc.date.issued","2021"],["dc.date.updated","2022-02-09T13:21:24Z"],["dc.description.abstract","Abstract Aim It is widely accepted that biodiversity is influenced by both niche‐related and spatial processes from local to global scales. Their relative importance, however, is still disputed, and empirical tests are surprisingly scarce at the global scale. Here, we compare the importance of area (as a proxy for pure spatial processes) and environmental heterogeneity (as a proxy for niche‐related processes) for predicting native mammal species richness world‐wide and within biogeographical regions. Location Global. Time period We analyse a spatial snapshot of richness data collated by the International Union for Conservation of Nature. Major taxa studied All terrestrial mammal species, including possibly extinct species and species with uncertain presence. Methods We applied a spreading dye algorithm to analyse how native mammal species richness changes with area and environmental heterogeneity. As measures for environmental heterogeneity, we used elevation ranges and precipitation ranges, which are well‐known correlates of species richness. Results We found that environmental heterogeneity explained species richness relationships better than did area, suggesting that niche‐related processes are more prevalent than pure area effects at broad scales. Main conclusions Our results imply that niche‐related processes are essential to understand broad‐scale species–area relationships and that habitat diversity is more important than area alone for the protection of global biodiversity."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659"],["dc.identifier.doi","10.1111/geb.13261"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/80524"],["dc.language.iso","en"],["dc.notes.intern","DeepGreen Import"],["dc.relation.issn","1466-822X"],["dc.relation.issn","1466-8238"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.relation.orgunit","Professuren für Statistik und Ökonometrie"],["dc.relation.orgunit","Abteilung Agrarökologie"],["dc.rights","This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited."],["dc.title","Environmental heterogeneity predicts global species richness patterns better than area"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2240"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","Methods in Ecology and Evolution"],["dc.bibliographiccitation.lastpage","2248"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Sciaini, Marco"],["dc.contributor.author","Fritsch, Matthias"],["dc.contributor.author","Scherer, Cédric"],["dc.contributor.author","Simpkins, Craig Eric"],["dc.contributor.editor","Golding, Nick"],["dc.date.accessioned","2020-12-10T18:26:40Z"],["dc.date.available","2020-12-10T18:26:40Z"],["dc.date.issued","2018"],["dc.description.abstract","1. Neutral landscape models (NLMs) simulate landscape patterns based on theoretical distributions and can be used to systematically study the effect of landscape structure on ecological processes. NLMs are commonly used in landscape ecology to enhance the findings of field studies as well as in simulation studies to provide an underlying landscape. However, their creation so far has been limited to software that is platform dependent, does not allow a reproducible workflow or is not embedded in R, the prevailing programming language used by ecologists. 2. Here, we present two complementary R packages NLMR and landscapetools, that allow users to generate and manipulate NLMs in a single environment. They grant the simulation of the widest collection of NLMs found in any single piece of software thus far while allowing for easy manipulation in a self‐contained and reproducible workflow. The combination of both packages should stimulate a wider usage of NLMs in ecology. NLMR is a comprehensive collection of algorithms with which to simulate NLMs. landscapetools provides a utility toolbox which facilitates an easy workflow with simulated neutral landscapes and other raster data. 3. We show two example applications that illustrate potential use cases for NLMR and landscapetools: First, an agent‐based simulation study in which the effect of spatial structure on disease persistence was studied. The second example shows how increases in spatial scaling can introduce biases in calculated landscape metrics. 4. Simplifying the workflow around generating and handling NLMs should encourage an uptake in the usage of NLMs. NLMR and landscapetools are both generic frameworks that can be used in a variety of applications and are a further step to having a unified simulation environment in R for answering spatial research questions."],["dc.identifier.doi","10.1111/2041-210X.13076"],["dc.identifier.eissn","2041-210X"],["dc.identifier.issn","2041-210X"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76152"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation.issn","2041-210X"],["dc.relation.issn","2041-210X"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.title","NLMR and landscapetools: An integrated environment for simulating and modifying neutral landscape models in R"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1368"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","Methods in Ecology and Evolution"],["dc.bibliographiccitation.lastpage","1378"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Fritsch, Matthias"],["dc.contributor.author","Lischke, Heike"],["dc.contributor.author","Meyer, Katrin M."],["dc.contributor.editor","Murrell, David"],["dc.date.accessioned","2020-12-08T08:09:48Z"],["dc.date.available","2020-12-08T08:09:48Z"],["dc.date.issued","2020"],["dc.description.abstract","Modelling is often confronted with scaling problems, because modelling—directly or indirectly—always implies scaling. This is because models simplify. Simplification usually means aggregation, and aggregation is a scaling process. As scaling cannot be avoided in modelling, it should carefully be addressed and resolved, at least to the degree possible. In this paper, we give an overview of scaling approaches in ecological modelling. We propose to classify scaling approaches into pre-model scaling, in-model scaling and post-model scaling depending on the timing of the scaling relative to the main modelling process. We show general approaches, examples and potential application problems for each category. We suggest that scaling problems might be more widespread than previously thought. These scaling problems are matched with a range of solutions, but often these solutions will have to be adapted and tailored to the specific scaling case. Thus, we recommend that ecologists be aware of scaling challenges especially where models do not explicitly aim at scaling. Developing general test systems for scaling methods may help to broaden and enhance the application of scaling methods in ecology."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659"],["dc.identifier.doi","10.1111/2041-210X.13466"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/69462"],["dc.language.iso","en"],["dc.notes.intern","DeepGreen Import"],["dc.relation.issn","2041-210X"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.rights","This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes."],["dc.subject.gro","aggregation"],["dc.subject.gro","ecological model"],["dc.subject.gro","levels of scale"],["dc.subject.gro","meta-model"],["dc.subject.gro","pattern-process relationship"],["dc.subject.gro","scale transition theory"],["dc.subject.gro","scaling-down"],["dc.subject.gro","scaling-up"],["dc.title","Scaling methods in ecological modelling"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]