The rare Himalayan wolf Canis lupus chanco is categorized as Vulnerable on the IUCN Red List, and there is limited knowledge of its ecology and distribution. In Bhutan, the Himalayan wolf is one of the least known carnivores. Our aims in this study were to map the current distribution of the wolf in Bhutan and to identify potential habitats within the country. We compiled 32 records of wolf presence from camera-trap surveys and, using a maximum entropy approach, we estimated a potential habitat of 2,431 km2, comprising c. 6.3% of Bhutan. However, wolf presence was localized and non-continuous. We recommend a detailed fine-scale habitat analysis in areas of potential habitat and genetic studies to investigate population structure. Knowledge of these matters will provide insights regarding connectivity and facilitate the development of conservation strategies for this threatened wolf.

Several authors have investigated the question of whether canonical logic-based accounts of belief revision, and especially the theory of AGM revision operators, are compatible with the dynamics of Bayesian conditioning. Here we show that Leitgeb’s stability rule for acceptance, which has been offered as a possible solution to the Lottery paradox, allows to bridge AGM revision and Bayesian update: using the stability rule, we prove that AGM revision operators emerge from Bayesian conditioning by an application of the principle of maximum entropy. In situations of information loss, or whenever the agent relies on a qualitative description of her information state—such as a plausibility ranking over hypotheses, or a belief set—the dynamics of AGM belief revision are compatible with Bayesian conditioning; indeed, through the maximum entropy principle, conditioning naturally generates AGM revision operators. This mitigates an impossibility theorem of Lin and Kelly for tracking Bayesian conditioning with AGM revision, and suggests an approach to the compatibility problem that highlights the information loss incurred by acceptance rules in passing from probabilistic to qualitative representations of belief.

Habitat prioritization and corridor restoration are important steps for reconnecting fragmented habitats and species populations, and spatial modelling approaches are useful in identifying suitable habitat for elusive tropical rainforest mammals. The Endangered Bornean banteng Bos javanicus lowi, a wild bovid endemic to Borneo, occurs in habitat that is highly fragmented as a result of extensive agricultural expansion. Based on the species’ historical distribution in Sabah (Malaysia), we conducted camera-trap surveys in 14 forest reserves during 2011–2016. To assess suitable habitat for the banteng we used a presence-only maximum entropy (MaxEnt) approach with 11 spatial predictors, including climate, infrastructure, land cover and land use, and topography variables. We performed a least-cost path analysis using Linkage Mapper, to understand the resistance to movement through the landscape. The surveys comprised a total of 44,251 nights of camera trapping. We recorded banteng presence in 11 forest reserves. Key spatial predictors deemed to be important in predicting suitable habitat included soil associations (52.6%), distance to intact and logged forests (11.8%), precipitation in the driest quarter (10.8%), distance to agro-forest and regenerating forest (5.7%), and distance to oil palm plantations (5.1%). Circa 11% of Sabah had suitable habitat (7,719 km2), of which 12.2% was in protected forests, 60.4% was in production forests and 27.4% was in other areas. The least-cost path model predicted 21 linkages and a relatively high movement resistance between core habitats. Our models provide information about key habitat and movement resistance for bantengs through the landscape, which is crucial for constructive conservation strategies and land-use planning.

In modelling joint probability distributions it is often desirable to incorporate standard marginal distributions and match a set of key observed mixed moments. At the same time it may also be prudent to avoid additional unwarranted assumptions. The problem is to find the least ordered distribution that respects the prescribed constraints. In this paper we will construct a suitable joint probability distribution by finding the checkerboard copula of maximum entropy that allows us to incorporate the appropriate marginal distributions and match the nominated set of observed moments.

Throughout the world, the invasion of nonnative plants is an increasing threat to native biodiversity and ecosystem sustainability. Invasion is especially prevalent in areas affected by land transformation and disturbance. Surface mines are a major land transformation, and thus may promote the establishment and persistence of invasive plant communities. Using the Shale Hills region of Alabama as a case study, we assessed the use of landscape characteristics in predicting the probability of occurrence of six invasive plant species: sericea lespedeza, Japanese honeysuckle, Chinese privet, autumn-olive, royal paulownia, and sawtooth oak. Models were generated for invasive species occurrence using logistic regression and maximum entropy methods. The predicted probabilities of species occurrence were applied to the mined landscape to assess the probable prevalence of each species across the landscape. Japanese honeysuckle had the highest probable prevalence on the landscape (48% of the area), with royal paulownia having the lowest (less than 1%). Overall, 67% of the landscape was predicted to have at least one invasive plant species, with 20% of the landscape predicted to have two or more species, and 3% of the landscape predicted to have three or more species. Japanese honeysuckle, sericea lespedeza, privet, and autumn-olive showed higher occurrence on the reclaimed sites than across the broader region. We found that geospatial modeling of these invasive plants at this scale offered potential for management, both for identifying habitat types at risk and areas that need management attention. However, the most immediate action for reducing the prevalence of invasive plants on reclaimed mines is to remove invasive plants from the reclamation planting list. Three (sericea lespedeza, autumn-olive, and sawtooth oak) out of the six most common invasive plants in this study were planted as part of reclamation activities.

Data on the number of Pennsylvania dairy farms by size category are analyzed in a Markov chain setting to determine factors affecting entry, exit, expansion, and contraction within the sector. Milk prices, milk price volatility, land prices, policy, and cow productivity all impact structural change in Pennsylvania's dairy sector. Stochastic simulation analysis suggests that the number of dairy farms in Pennsylvania will likely fall by only 2.0 percent to 2.5 percent annually over the next 20 years, indicating that dairy farming in Pennsylvania is likely to be a significant enterprise for the state in the foreseeable future.

Juggler's exclusion process describes a system of particles on the positive integers where particles drift down to zero at unit speed. After a particle hits zero, it jumps into a randomly chosen unoccupied site. We model the system as a set-valued Markov process and show that the process is ergodic if the family of jump height distributions is uniformly integrable. In a special case where the particles jump according to a set-avoiding memoryless distribution, the process reaches its equilibrium in finite nonrandom time, and the equilibrium distribution can be represented as a Gibbs measure conforming to a linear gravitational potential.

Let S: [0, 1]→[0, 1] be a chaotic map and let f* be a stationary density of the Frobenius-Perron operator PS: L1→L1 associated with S. We develop a numerical algorithm for approximating f*, using the maximum entropy approach to an under-determined moment problem and the Chebyshev polynomials for the stability consideration. Numerical experiments show considerable improvements to both the original maximum entropy method and the discrete maximum entropy method.

The distribution of the Near Threatened Caucasian grouse Tetrao mlokosiewiczi, endemic to the Caucasus, was examined to model the species’ nesting habitat, and thus facilitate its conservation and the identification of Key Biodiversity Areas in the Caucasus. The species’ occurrence was defined by field surveys and radio-telemetry. Data were managed and analysed using a geographical information system and various modelling techniques. Grouse locations were divided into training and testing datasets. Habitat variables measured at training locations were used to develop models, and testing locations were used to validate the models. The final best-fit model suggested that Caucasian grouse prefer open habitat, and the most important independent variables accounting for the species' distribution were annual mean temperature, mean temperature of warmest quarter, precipitation seasonality and proximity to deciduous broad-leaf forest. The incorporation of human disturbance and ruggedness into the final model significantly increased its predictive power. This model provides a tool to improve search effectiveness for Caucasian grouse in the Caucasus and for the conservation and management of the species. The model can predict the probable distribution of Caucasian grouse and the corridors between known populations. Threatened and endemic species are often used as species for setting site-based conservation priorities, and this habitat model could help to identify new Key Biodiversity Areas for protection in the Caucasus. The Ministry of Environmental Protection and Natural Resources of Georgia is going to use the results of this study to reshape existing protected areas and identify new ones.

The crystal structure of ligand-free tryptophanyl-tRNA synthetase (TrpRS) was solved at 2.9 Å using a combination of molecular replacement and maximum-entropy map/phase improvement. The dimeric structure (R = 23.7, Rfree = 26.2) is asymmetric, unlike that of the TrpRS tryptophanyl–5′AMP complex (TAM; Doublié S, Bricogne G, Gilmore CJ, Carter CW Jr, 1995, Structure 3:17–31). In agreement with small-angle solution X-ray scattering experiments, unliganded TrpRS has a conformation in which both monomers open, leaving only the tryptophan-binding regions of their active sites intact. The amino terminal αA-helix, TIGN, and KMSKS signature sequences, and the distal helical domain rotate as a single rigid body away from the dinucleotide-binding fold domain, opening the AMP binding site, seen in the TAM complex, into two halves. Comparison of side-chain packing in ligand-free TrpRS and the TAM complex, using identification of nonpolar nuclei (Ilyin VA, 1994, Protein Eng 7:1189–1195), shows that significant repacking occurs between three relatively stable core regions, one of which acts as a bearing between the other two. These domain rearrangements provide a new structural paradigm that is consistent in detail with the “induced-fit” mechanism proposed for TyrRS by Fersht et al. (Fersht AR, Knill-Jones JW, Beduelle H, Winter G, 1988, Biochemistry 27:1581–1587). Coupling of ATP binding determinants associated with the two catalytic signature sequences to the helical domain containing the presumptive anticodon-binding site provides a mechanism to coordinate active-site chemistry with relocation of the major tRNA binding determinants.

An important question in the study of moment problems is to determine when a fixed point in ℝn lies in the moment cone of vectors , with μ a nonnegative measure. In associated optimization problems it is also important to be able to distinguish between the interior and boundary of the moment cone. Recent work of Dachuna-Castelle, Gamboa and Gassiat derived elegant computational characterizations for these problems, and for related questions with an upper bound on μ. Their technique involves a probabilistic interpretation and large deviations theory. In this paper a purely convex analytic approach is used, giving a more direct understanding of the underlying duality, and allowing the relaxation of their assumptions.

A generalization of the notion of a stationary Markov chain in more than one dimension is proposed, and is found to be a special class of homogeneous Markov random fields. Stationary Markov chains in many dimensions are shown to possess a maximum entropy property, analogous to the corresponding property for Markov chains in one dimension. In addition, a representation of Markov chains in many dimensions is provided, together with a method for their generation that converges to their stationary distribution.

The problem of image reconstruction from incomplete and noisy complex Fourier spectrum is considered. The maximum entropy method (MEM) is of great interest as the most effective nonlinear reconstruction method having superresolution effect. Because objects of radio astronomical observations are incoherent radio sources described by real non-negative distributions, application of the classical MEM is quite reasonable. But it is established that the MEM gives acceptable reconstruction quality mostly in the case of point-like sources and in general it does not ensure satisfactory reconstruction of continous, graytone objects, which can considerably restrict applications of the MEM in astronomy. The generalized maximum entropy method (GMEM) was originally proposed for reconstruction of distributions described by complex functions (Bajkova, 1992) and was considered as having the same properties of the classical MEM. More careful analysis of the GMEM and classical MEM for real non-negative objects allowed to establish that the GMEM ensures much more exact reconstruction, especially in the case of continous objects. Explanation and demonstration of this interesting and very important phenomenon is the purpose of the present paper.