Accurate modeling of geographic distributions of species is crucial to various applications in ecology and conservation. The best performing techniques often require some parameter tuning, which may be prohibitively time-consuming to do separately for each species, or unreliable for small or biased datasets. Additionally, even with the abundance of good quality data, users interested in the application of species models need not have the statistical knowledge required for detailed tuning. In such cases, it is desirable to use ‘‘default settings’’, tuned and validated on diverse datasets. Maxent is a recently introduced modeling technique, achieving high predictive accuracy and enjoying several additional attractive properties. The performance of Maxent is influenced by a moderate number of parameters. The first contribution of this paper is the empirical tuning of these parameters. Since many datasets lack information about species absence, we present a tuning method that uses presence-only data. We evaluate our method on independently collected high-quality presenceabsence data. In addition to tuning, we introduce several concepts that improve the predictive accuracy and running time of Maxent. We introduce ‘‘hinge features’ ’ that model more complex relationships in the training data; we describe a new logistic output format that gives an estimate of probability of presence; finally we explore ‘‘background sampling’’ strategies that cope with sample selection bias and decrease model-building time. Our evaluation, based on a diverse dataset of 226 species from 6 regions, shows: 1) default settings tuned on presence-only data achieve performance which is almost as good as if they had been tuned on the evaluation data itself; 2) hinge features substantially improve model

Abstract. We have tested the hypothesis that coarse-scale environmental features are associated with spatial variation in bovine tuberculosis (BTB) prevalence, based on extensive sampling and testing of cattle in the state of Jalisco, Mexico. Ecological niche models were developed to summarize relationships between BTB occurrences and aspects of climate, topography and surface. Model predictions, however, reflected the distributions of dairy cattle versus beef cattle, and the non-random nature of sampling any cattle, but did not succeed in detecting environmental correlates at spatial resolutions of 1 km. Given that the tests employed seek any predictivity better than random expectations, making the finding of no environmental associations conservative, we conclude that BTB prevalence is independent of coarsescale environmental features.

Non-indigenous species (NIS) are species that are moved by man outside their native range. NIS that successfully pass through a number of invasion phases (i.e. introduction, establishment and invasion) are referred to as invasive species. Invasive species potentially cause severe environmental and economic impacts. This thesis highlights the invasion process of non-indigenous aquatic plant species. Patterns and processes in the establishment phase were assessed with a general focus. The importance of recipient area biodiversity for habitat invasibility was assessed by comparing native species richness in lakes where non-indigenous plant species had become established with non-invaded lakes. It was shown that habitats with established NIS on average had higher native species richness. Ecological niche modelling was used to test how the continental-scale distributions of non-indigenous freshwater plants in Europe might change during global warming. These models showed that species presently invasive in southern Europe can potentially establish also in northern Europe if the climate gets warmer. At the invasion phase, focus was more specific to a single species – the floating-leaved

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Remote sensing-based predictors improve distribution