Terrestrial long-distance migrations are declining globally: in North America, nearly 75% have been lost. Yet there has been limited research comparing habitat suitability and connectivity models to identify migration corridors across increasingly fragmented landscapes. Here we use pronghorn (Antilocapra americana) migrations in prairie habitat to compare two types of models that identify habit...

Species distribution modeling (SDM) is an important tool to assess the impact of global environmental change. Many species exhibit ecologically relevant intraspecific variation, and few studies have analyzed its relevance for SDM. Here, we compared three SDM techniques for the highly variable species Pinus contorta. First, applying a conventional SDM approach, we used MaxEnt to model the subjec...

Ill-posed inverse problems of the form y = Xp where y is J-dimensional vector of a data, p is m-dimensional probability vector which can not be measured directly and matrix X of observable variables is a known J ×m matrix, J < m, are frequently solved by Shannon’s entropy maximization (MaxEnt, ME). Several axiomatizations were proposed (see for instance [1], [2], [3], [4], [5], [6], [7], [8], a...

The task of inferring the native language of an author based on texts written in a second language has generally been tackled as a classification problem, typically using as features a mix of n-grams over characters and part of speech tags (for small and fixed n) and unigram function words. To capture arbitrarily long n-grams that syntax-based approaches have suggested are useful, adaptor gramm...

BACKGROUND Predicting species' potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. METHODOLOGY We collected the dis...

Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).

BACKGROUND Anopheles albimanus is among the most important vectors of human malaria in Mesoamerica and the Caribbean Basin (M-C). Here, we use topographic data and 1950-2000 climate (near present), and future climate (2080) layers obtained from general circulation models (GCMs) to project the probability of the species' presence, p(s), using the species distribution model MaxEnt. RESULTS The ...

1. Two dominant drivers of species distributions are climate and habitat, both of which are changing rapidly. Understanding the relative importance of variables that can control distributions is critical, especially for invasive species that may spread rapidly and have strong effects on ecosystems. 2. Here, we examine the relative importance of climate and habitat variables in controlling the d...

In this paper, we establish an equivalence between two conceptually different methods of signal estimation under modeling uncertainty viz. set–theoretic estimation and maximum entropy (maxent) MAP estimation. The first method assumes constraints on the signal to be estimated, the second assumes constraints on a probability distribution for the signal. We provide broad conditions under which the...