TY - JOUR A1 - Kärcher, Oskar A1 - Frank, Karin A1 - Walz, Ariane A1 - Markovic, Danijela T1 - Scale effects on the performance of niche-based models of freshwater fish distributions JF - Ecological modelling : international journal on ecological modelling and engineering and systems ecolog N2 - Niche-based species distribution models (SDMs) have become an essential tool in conservation and restoration planning. Given the current threats to freshwater biodiversity, it is of fundamental importance to address scale effects on the performance of niche-based SDMs of freshwater species’ distributions. The scale effects are addressed here in the context of hierarchical catchment ordering, considered as counterpart to coarsening grain-size by increasing grid-cell size. We combine fish occurrence data from the Danube River Basin, the hierarchical catchment ordering and multiple environmental factors representing topographic, climatic and anthropogenic effects to model fish occurrence probability across multiple scales. We focus on 1st to 5th order catchments. The spatial scale (hierarchical catchment order) only marginally influences the mean performance of SDMs, however the uncertainty of the estimates increases with scale. Key predictors and their relative importance are scale and species dependent. Our findings have useful implications for choosing proper species dependent spatial scales for river rehabilitation measures, and for conservation planning in areas where fine grain species data are unavailable. KW - Catchment order KW - Conservation planning KW - Danube KW - Generalized additive models KW - Species distribution modelling Y1 - 2019 U6 - https://doi.org/10.1016/j.ecolmodel.2019.05.006 SN - 0304-3800 SN - 1872-7026 VL - 405 SP - 33 EP - 42 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Markovic, Danijela A1 - Walz, Ariane A1 - Kärcher, Oskar T1 - Scale effects on the performance of niche-based models of freshwater fish distributions: Local vs. upstream area influences JF - Ecological modelling : international journal on ecological modelling and engineering and systems ecolog N2 - Niche-based species distribution models (SDMs) play a central role in studying species response to environmental change. Effective management and conservation plans for freshwater ecosystems require SDMs that accommodate hierarchical catchment ordering and provide clarity on the performance of such models across multiple scales. The scale-dependence components considered here are: (a) environment spatial structure, represented by hierarchical catchment ordering following the Strahler system; (b) analysis grain, that included 1st to 5th order catchments; and (c) response grain, the grain at which species respond most, represented by local and upstream catchment area effects. We used fish occurrence data from the Danube River Basin and various factors representing climate, land cover and anthropogenic pressures. Our results indicate that the choice of response grain local vs. upstream area effects and the choice of analysis grain, only marginally influence the performance of SDMs. Upstream effects tend to better predict fish distributions than corresponding local effects for anthropogenic and land cover factors, in particular for species sensitive to pollution. Key predictors and their relative importance are scale and species dependent. Consequently, choosing proper species dependent spatial scales and factors is imperative for effective river rehabilitation measures. KW - Catchment order KW - Conservation planning KW - Danube KW - Freshwater fish KW - Species distribution modelling KW - Upstream area Y1 - 2019 U6 - https://doi.org/10.1016/j.ecolmodel.2019.108818 SN - 0304-3800 SN - 1872-7026 VL - 411 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Häring, Tim A1 - Reger, Birgit A1 - Ewald, Jörg A1 - Hothorn, Torsten A1 - Schröder-Esselbach, Boris T1 - Predicting Ellenberg's soil moisture indicator value in the Bavarian Alps using additive georegression JF - Applied vegetation science : official organ of the International Association for Vegetation Science N2 - Questions Can forest site characteristics be used to predict Ellenberg indicator values for soil moisture? Which is the best averaged mean value for modelling? Does the distribution of soil moisture depend on spatial information? Location Bavarian Alps, Germany. Methods We used topographic, climatic and edaphic variables to model the mean soil moisture value as found on 1505 forest plots from the database WINALPecobase. All predictor variables were taken from area-wide geodata layers so that the model can be applied to some 250 000 ha of forest in the target region. We adopted methods developed in species distribution modelling to regionalize Ellenberg indicator values. Therefore, we use the additive georegression framework for spatial prediction of Ellenberg values with the R-library mboost, which is a feasible way to consider environmental effects, spatial autocorrelation, predictor interactions and non-stationarity simultaneously in our data. The framework is much more flexible than established statistical and machine-learning models in species distribution modelling. We estimated five different mboost models reflecting different model structures on 50 bootstrap samples in each case. Results Median R2 values calculated on independent test samples ranged from 0.28 to 0.45. Our results show a significant influence of interactions and non-stationarity in addition to environmental covariates. Unweighted mean indicator values can be modelled better than abundance-weighted values, and the consideration of bryophytes did not improve model performance. Partial response curves indicate meaningful dependencies between moisture indicator values and environmental covariates. However, mean indicator values <4.5 and >6.0 could not be modelled correctly, since they were poorly represented in our calibration sample. The final map represents high-resolution information of site hydrological conditions. Conclusions Indicator values offer an effect-oriented alternative to physically-based hydrological models to predict water-related site conditions, even at landscape scale. The presented approach is applicable to all kinds of Ellenberg indicator values. Therefore, it is a significant step towards a new generation of models of forest site types and potential natural vegetation. KW - Boosting KW - Mboost KW - Non-stationarity KW - Predictive vegetation mapping KW - Site ecology KW - Species distribution modelling Y1 - 2013 U6 - https://doi.org/10.1111/j.1654-109X.2012.01210.x SN - 1402-2001 VL - 16 IS - 1 SP - 110 EP - 121 PB - Wiley-Blackwell CY - Hoboken ER - TY - THES A1 - von dem Bussche, Jens T1 - Modelling the spatial distribution of blackbird (Turdus merula) and ring ouzel (Turdus torquatus) in Switzerland N2 - To characterise the habitat preferences of ring ouzel (Turdus torquatus) and blackbird (T. merula) in Switzerland, we adopt species distribution modelling and predict the species’ spatial distribution. We model on two different scales to analyse in how far downscaling leads to a different set of predictors to describe the realised habitat best. While the models on macroscale (grid of one square kilometre) cover the entire country, we select a set of smaller plots for modelling on territory scale. Whereas ring ouzels occur in altitudes above 1’000 m a.s.l. only, blackbirds occur from the lowlands up to the timber line. The altitudinal range overlap of the two species is up to 400 m. Despite both species coexist on macroscale, a direct niche overlap on territory scale is rare. Small-scale differences in vegetation cover and structure seem to play a dominant role for habitat selection. On macroscale however, we observe a high dependency on climatic variables mainly representing the altitudinal range and the related forest structure preferred by the two species. Applying the models for climate change scenarios, we predict a decline of suitable habitat for the ring ouzel with a simultaneous median altitudinal shift of +440 m until 2070. In contrast, the blackbird is predicted to benefit from higher temperatures and expand its range to higher elevations. N2 - Unter Verwendung von Habitatmodellen beschreiben wir die Habitatpräferenz von Amsel (Turdus merula) und Ringdrossel (T. torquatus) in der Schweiz. Mit Hilfe verschiedener Klimaszenarien prognostizieren wir zudem die künftige potentielle Verbreitung beider Arten. Zur Beschreibung eines eventuell auftretenden Skalensprungs, d.h. einer Änderung in der Beschreibungskraft der Variablen auf verschiedenen räumlichen Ebenen, erstellten wir Modelle auf zwei unterschiedlichen Skalen. Während das Modell auf Makroskala mit einer Maschenweite von einem Quadratkilometer die gesamte Schweiz abdeckt, erstellten wir zudem eine Auswahl an Untersuchungsgebieten auf Revierebene. Ringdrosseln zeigen ihren Verbreitungsschwerpunkt in der subalpinen Lage, während Amseln vornehmlich das Tiefland und die Tallagen besiedeln und nur vereinzelt in hohe Lagen vordringen. In einem Gürtel von ungefähr 400 Höhenmetern siedeln beide Arten parallel.Trotz dieses auf der Makroskala erkennbaren Überschneidungsbereiches konnten wir in unserer Untersuchung auf Revierebene, von einer Ausnahme abgesehen, keine Koexistenz beobachten. Kleinräumige Unterschiede in der Habitatstruktur, insbesondere in der Vegetationsbedeckung scheinen demnach für die Habitatselektion von maßgeblicher Bedeutung zu sein. Auf Makroebene hingegen wurde der Einfluss klimatischer Variablen deutlich, die neben der Höhenlage auch dort typische Vegetationsstrukturen widerspiegeln. Wie die Klimaszenarien zeigen, nehmen geeignete Ringdrosselhabitate bei steigenden Temperaturen ab und die Art weicht im Mittel um 440 m in höhere Lagen zurück. Für Amseln scheint sich eine zunehmende Erwärmung jedoch positiv auszuwirken, während das Verbreitungsgebiet im Tiefland beibehalten wird, dringt sie von den Tälern ausgehend zunehmend in höhere Lagen vor. KW - Klimaszenarien KW - Habitatpräferenz KW - Skalenabhängigkeit KW - Habitatmodellierung KW - Turdus sp. KW - Climate change scenarios KW - Habitat preferences KW - Scale dependency KW - Species distribution modelling KW - Turdus sp. Y1 - 2006 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-14012 ER -