Potential of Multi-Temporal Remote Sensing Data for Modeling Tree Species Distributions and Species Richness in Mexico

Julius-Maximilians-Universität Würzburg Institut für Geographie und Geologie Lehrstuhl für Fernerkundung Potential of multi-temporal remote sensing data for modeling tree species distributions and species richness in Mexico Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Julius-Maximilians-Universität Würzburg vorgelegt von Anna Cord Würzburg, März 2012 Eingereicht am: 06.03.2012 1. Gutachter: Prof. Dr. Stefan Dech 2. Gutachter: Prof. Dr. Björn Reineking 1. Prüfer: Prof. Dr. Stefan Dech 2. Prüfer: Prof. Dr. Jürgen Rauh Mentorin: Dr. Doris Klein Tag der Disputation: 09.05.2012 Abstract Current changes of biodiversity result almost exclusively from human activities. This anthropogenic conversion of natural ecosystems during the last decades has led to the so-called ‘biodiversity crisis’, which comprises the loss of species as well as changes in the global distribution patterns of organisms. These modifications in species functional traits available in a certain area are intimately connected with the loss of ecosystem services, from which we humans directly and indirectly benefit. To highlight this causal relationship between biodiversity and human well-being, the United Nations has declared the years 2011 to 2020 as the ‘Decade of Biodiversity’. Species richness is unevenly distributed worldwide. Altogether, 17 so-called ‘megadiverse’ nations cover less than 10% of the earth’s land surface but support nearly 70% of global species richness. Mexico, the study area of this thesis, is one of those countries. Due to this special responsibility for global biodiversity, Mexico has put particular emphasis on assessing its biota. Already in 1992, the National Commission for the Knowledge and Use of Biodiversity (CONABIO) was established. The country today has a unique availability of species occurrence data from different sources including the National Information System on Mexican Biodiversity (SNIB) and the National Forest Inventory (INFyS). However, due to Mexico’s large extent and geographical complexity, it is impossible to conduct reliable and spatially explicit assessments of species distribution ranges based on these collection data and field work alone. In the last two decades, Species distribution models (SDMs) have been established as important tools for extrapolating such in situ observations. SDMs analyze empirical correlations between geo-referenced species occurrence data and environmental variables to obtain spatially explicit surfaces indicating the probability of species occurrence. Remote sensing can provide such variables which describe biophysical land surface characteristics with high effective spatial resolutions. Especially during the last three to five years, the number of studies making use of remote sensing data for modeling species distributions has therefore multiplied. Due to the novelty of this field of research, the published literature consists mostly of selective case studies. A systematic framework for modeling species distributions by means of remote sensing is still missing. This research gap was taken up by this thesis and specific studies were designed which addressed the combination of climate and remote sensing data in SDMs, the suitability of continuous remote sensing variables in comparison with categorical land cover classification data, the criteria for selecting appropriate remote sensing data depending on species characteristics, and the effects of inter-annual variability in remotely sensed time series on the performance of species distribution models. The corresponding novel analyses were conducted with the Maximum Entropy algorithm developed by Phillips et al. (2004). In this thesis, a more comprehensive set of remote sensing predictors than in the existing literature was utilized for species distribution modeling. The products were selected based on their ecological relevance for characterizing species distributions. Two 1 km Terra-MODIS Land 16-day composite standard products including the Enhanced I II Abstract Vegetation Index (EVI), Reflectance Data, and Land Surface Temperature (LST) were assembled into enhanced time series for the time period of 2001 to 2009. These high- dimensional time series data were then transformed into 18 phenological and 35 statistical metrics that were selected based on an extensive literature review. Spatial distributions of twelve tree species were modeled in a hierarchical framework which integrated climate (WorldClim) and MODIS remote sensing data. The species are representative of the major Mexican forest types and cover a variety of ecological traits, such as range size and biotope specificity. Trees were selected because they have a high probability of detection in the field and since mapping vegetation has a long tradition in remote sensing. The major findings of this thesis can be summarized as: Spatial autocorrelation (SAC, measured by Moran’s I) was significantly lower for remote sensing than for interpolated climate data. Due to the inherent SAC of species occurrence data, a non-hierarchical model design would therefore not fully exploit the potential of remote sensing data. The hierarchical modeling approach implemented in this thesis outperformed purely climatic and remote sensing based models by improving the effective spatial resolution of the environmental predictors and integrating additional constraints on the species distributions. Due to the inclusion of remote sensing data, the fractional predicted area (FPA) was reduced by up to 80% per species. The comparison of continuous remote sensing data and an existing categorical land cover classification showed that the general overestimation of species distribution ranges based on categorical land cover information was influenced by how closely the spatial distribution patterns of a species could be linked to certain land cover types. A novel land cover evenness index was introduced which quantifies this species-land cover relationship and showed a linear correlation (r² = 0.54) with the model improvement measured by the increase in AUC (area under curve) scores. Beyond this, the relevance of different remote sensing products to the model results was determined: Vegetation indices are the most widely used but not necessarily the best predictors of species distributions. Instead, the feature ranking indicated a high potential of LST – in particular for temperate species. Spectral bands in turn were only of minor importance, except for the mangrove species occurring in coastal wetlands. The use of longer remotely sensed time series generally improved the performance of species distribution models. However, the impact greatly differed from 0.6% up to 30.0% reduction in model deviance between species. Wide-ranging species (≥ 300 presence records) generally featured less variation in model deviance. In addition, the so-called stacked species distribution modeling approach was applied to compile the first Mexican pine richness map based on 40 species of the tree genus Pinus (family Pinaceae). Mexico has the greatest number of pine species of any region of similar size in the world. The species distribution ranges modeled with the hierarchical combination of both bioclimatic and remote sensing data yielded the best accuracy with the lowest overall deviance (0.169) from independent presence-absence data across all species. A site-specific evaluation further showed that the combined model was significantly better in predicting species richness than the purely climatic model. The analysis thus confirmed the potential of remote sensing data not only for obtaining better estimates of individual species distributions but also for predicting species richness. Zusammenfassung Sämtliche aktuell zu beobachtenden Veränderungen in der Biodiversität lassen sich fast ausschließlich auf menschliche Aktivitäten zurückführen. In den letzten Jahrzehnten hat insbesondere die anthropogene Umwandlung bisher unberührter, natürlicher Ökosysteme zur sogenannten ‚Biodiversitätskrise‘ geführt. Diese umfasst nicht nur das Aussterben von Arten, sondern auch räumliche Verschiebungen in deren Verbreitungsgebieten. Daraus wiederum ergeben sich Veränderungen in den jeweiligen funktionalen Eigenschaften (functional traits) der Arten, welche eng mit der Verfügbarkeit von Ökosystemdienstleistungen in der jeweiligen Region verknüpft sind, von denen wir Menschen direkt und indirekt profitieren. Um diesen kausalen Zusammenhang zwischen Biodiversität und menschlichem Wohl zu unterstreichen, wurde der Zeitraum von 2011 bis einschließlich 2020 von den Vereinten Nationen zur ‚Dekade der Biodiversität‘ erklärt. Global gesehen ist der Artenreichtum ungleich verteilt. Nur insgesamt 17 sogenannte ‚megadiverse‘ Länder, welche 10% der globalen Landoberfläche umfassen, beherbergen fast 70% der weltweiten Artenvielfalt. Mexiko, das Studiengebiet dieser Arbeit, ist eine dieser außerordentlich artenreichen Nationen. Aufgrund seiner besonderen Verantwortung für die globale Biodiversität wurde daher bereits im Jahr 1992 die Nationale Kommission für die Erforschung und Nutzung von Biodiversität (CONABIO) eingerichtet. Heute verfügt Mexiko über einzigartige Datenbanken über die Verteilung von Fundorten der einzelnen Arten im Land, wie beispielsweise das Nationale Informationssystem zur mexikanischen Biodiversität (SNIB) und das Nationale Forstinventar (INFyS). Aufgrund seiner großen Ausdehnung und geographischen Komplexität kann eine verlässliche und detaillierte räumliche Erfassung von Artverbreitungsgebieten in Mexiko

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