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Saimiri cassiquiarensis cassiquiarensis (Cebidae) is a primate subspecies with a wide distribution in the Amazonian region of Brazil, Colombia, and Venezuela. However, the boundaries of its geographic range remain poorly defined. This study presents new occurrence localities for this subspecies and updates its distribution using a compiled data set of 140 occurrence records based on literature, specimens vouchered in scientific collections, and new field data to produce model-based range maps. After cleaning our data set, we updated the subspecies' extent of occurrence, which was used in model calibration. We then modeled the subspecies' range using a maximum entropy algorithm (MaxEnt). The final model was adjusted using a fixed threshold, and we revised this polygon based on known geographic barriers and parapatric congeneric ranges. Our findings indicate that this subspecies is strongly associated with lowland areas, with consistently high daily temperatures. We propose modifications to all range boundaries and estimate that 3% of the area of occupancy (AOO, as defined by IUCN) has already been lost due to deforestation, resulting in a current range of 224,469 km2. We also found that 54% of their AOO is currently covered by protected areas (PAs). Based on these results, we consider that this subspecies is currently properly classified as Least Concern, because it occupies an extensive range, which is relatively well covered by PAs, and is currently experiencing low rates of deforestation.

Saimiri cassiquiarensis cassiquiarensis (Cebidae) é uma subespécie de primata com ampla distribuição na região amazônica do Brasil, Colômbia e Venezuela. No entanto, os limites de sua distribuição geográfica permanecem mal definidos. Este estudo apresenta novas localidades de ocorrência para essa subespécie e atualiza sua distribuição usando 140 registros de ocorrência compilados com base na literatura, espécimes depositados em coleções científicas e novos registros de campo para produzir mapas de distribuição baseados em modelos. Após a limpeza do nosso banco de dados, atualizamos a extensão de ocorrência da subespécie, que foi usada na calibração do modelo. Em seguida, modelamos a área de distribuição da subespécie usando um algoritmo de entropia máxima (MaxEnt). O modelo final foi ajustado usando um limiar fixo e revisamos esse polígono com base em barreiras geográficas conhecidas e na distribuição de congêneres parapátricas. Nosso modelo sugere que a espécie é fortemente associada a áreas planas, com temperaturas diárias consistentemente altas. Propomos modificações em todos os limites da área de distribuição e estimamos que 3% da área de ocupação (AOO, conforme definida pela IUCN) da subespécie já foi perdida devido ao desmatamento, resultando em uma área de distribuição atual de 224,469 km2. Também estimamos que 54% de sua AOO encontra­se atualmente coberta por áreas protegidas. Com base nesses resultados, consideramos que a subespécie está apropriadamente classificada como Pouco Preocupante, pois ocupa uma área extensa, que é relativamente bem coberta por áreas protegidas e atualmente apresenta baixas taxas de desmatamento.

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The security of a network requires the correct identification and characterization of the attacks through its ports. This involves the follow-up of all the requests for access to the networks by all kinds of users. We consider the frequency of connections and the type of connections to a network, and determine their joint probability. This leads to the problem of determining a joint probability distribution from the knowledge of its marginals in the presence of errors of measurement. Mathematically, this consists of an ill-posed linear problem with convex constraints, which we solved by the method of maximum entropy in the mean. This procedure is flexible enough to accommodate errors in the data in a natural way. Also, the procedure is model-free and, hence, it does not require fitting unknown parameters.

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The Chilean hazelnut (Gevuina avellana Mol., Proteaceae) is a native tree of Chile and Argentina of edible fruit-type nut. We applied two approaches to contribute to the development of strategies for mitigation of the effects of climate change and anthropic activities in G. avellana. It corresponds to the first report where both tools are integrated, the MaxEnt model to predict the current and future potential distribution coupled with High-Resolution Melting Analysis (HRM) to assess its genetic diversity and understand how the species would respond to these changes. Two global climate models: CNRM-CM6-1 and MIROC-ES2L for four Shared Socioeconomic Pathways: 126, 245, 370, and 585 (2021−2040; 2061−2080) were evaluated. The annual mean temperature (43.7%) and water steam (23.4%) were the key factors for the distribution current of G. avellana (AUC = 0.953). The future prediction model shows to the year 2040 those habitat range decreases at 50% (AUC = 0.918). The genetic structure was investigated in seven natural populations using eight EST-SSR markers, showing a percentage of polymorphic loci between 18.69 and 55.14% and low genetic differentiation between populations (Fst = 0.052; p < 0.001). According to the discriminant analysis of principal components (DAPC) we identified 10 genetic populations. We conclude that high-priority areas for protection correspond to Los Avellanos and Punta de Águila populations due to their greater genetic diversity and allelic richness.

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The financial market is a complex system in which the assets influence each other, causing, among other factors, price interactions and co-movement of returns. Using the Maximum Entropy Principle approach, we analyze the interactions between a selected set of stock assets and equity indices under different high and low return volatility episodes at the 2008 Subprime Crisis and the 2020 COVID-19 outbreak. We carry out an inference process to identify the interactions, in which we implement the a pairwise Ising distribution model describing the first and second moments of the distribution of the discretized returns of each asset. Our results indicate that second-order interactions explain more than 80% of the entropy in the system during the Subprime Crisis and slightly higher than 50% during the COVID-19 outbreak independently of the period of high or low volatility analyzed. The evidence shows that during these periods, slight changes in the second-order interactions are enough to induce large changes in assets correlations but the proportion of positive and negative interactions remains virtually unchanged. Although some interactions change signs, the proportion of these changes are the same period to period, which keeps the system in a ferromagnetic state. These results are similar even when analyzing triadic structures in the signed network of couplings.

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Electrical energy is generated in different ways, each located at some specific geographical area, and with different impact on the environment. Different sectors require heterogeneous rates of energy delivery, due to economic requirements. An important problem to solve is to determine how much energy must be sent from each supplier to satisfy each demand. Besides, the energy distribution process may have to satisfy ecological, technological, or economic cost constraints.

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The Thermodynamic Formalism provides a rigorous mathematical framework for studying quantitative and qualitative aspects of dynamical systems. At its core, there is a variational principle that corresponds, in its simplest form, to the Maximum Entropy principle. It is used as a statistical inference procedure to represent, by specific probability measures (Gibbs measures), the collective behaviour of complex systems. This framework has found applications in different domains of science. In particular, it has been fruitful and influential in neurosciences. In this article, we review how the Thermodynamic Formalism can be exploited in the field of theoretical neuroscience, as a conceptual and operational tool, in order to link the dynamics of interacting neurons and the statistics of action potentials from either experimental data or mathematical models. We comment on perspectives and open problems in theoretical neuroscience that could be addressed within this formalism.

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Framework-forming scleractinian (FFS) corals provide structurally complex habitats to support abundant and diverse benthic communities but are vulnerable to environmental changes and anthropogenic disturbances. Scientific modeling of suitable habitat provides important insights into the impact of the environmental conditions and fills the gap in the knowledge on habitat suitability. This study presents predictive habitat suitability modeling for deep-sea (depth > 50 m) FFS corals in the GoM. We first conducted a nonparametric estimate of the observed coral point process intensity as a function of each numeric environmental variable. Next, we performed species distribution modeling (SDM) using an assemble of four machine learning models - maximum entropy (ME), support vector machine (SVM), random forest (RF), and deep neural network (DNN). We found that most important variables controlling the coral distribution are super-dominant gravel and rock substrata, SW and SE aspects, slope steepness, salinity, depth, temperature, acidity, dissolved oxygen, and chlorophyll-a. Highly suitable habitats are predicted to be on the continental slope off Texas, Louisiana, and Mississippi and the shelf and slope of the West Florida Escarpment. All the four models have outstanding prediction performances with AUC values over 0.95. DNN model performs best (AUC = 0.987). The study contributes to coral habitat modeling research by presenting unique methods including nonparametric function of coral point process intensity, DNN and SVM models that have not been used in coral SDM, post-classification model assembling, and percentile approach to determine a threshold value for classifying a suitability score map into a binary map. Our findings would help support conservation prioritization, management and planning, and guide new field exploration.

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Human-mediated species invasions are recognized as a leading cause of global biotic homogenization and extinction. Studies on colonization events since early stages, establishment of new populations and range extension are scarce because of their rarity, difficult detection and monitoring. Chromis limbata is a reef-associated and non-migratory marine fish from the family Pomacentridae found in depths ranging between 3 and 45 m. The original distribution of the species encompassed exclusively the eastern Atlantic, including the Azores, Madeira and the Canary Islands. It is also commonly reported from West Africa between Senegal and Pointe Noire, Congo. In 2008, vagrant individuals of C. limbata were recorded off the east coast of Santa Catarina Island, South Brazil (27° 41' 44″ S, 48° 27' 53″ W). This study evaluated the increasing densities of C. limbata populations in Santa Catarina State shoreline. Two recent expansions, northwards to São Paulo State and southwards to Rio Grande do Sul State, are discussed, and a niche model of maximum entropy (MaxEnt) was performed to evaluate suitable C. limbata habitats. Brazilian populations are established and significantly increasing in most sites where the species has been detected. The distributional boundaries predicted by the model are clearly wider than their known range of occurrence, evidencing environmental suitability in both hemispheres from areas where the species still does not occur. Ecological processes such as competition, predation and specially habitat selectivity may regulate their populations and overall distribution range. A long-term monitoring programme and population genetics studies are necessary for a better understanding of this invasion and its consequences to natural communities.

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The sea louse (Caligus rogercresseyi) is the most relevant parasite for the farmed salmon industry in Chile, the second largest producer worldwide. Although spatial patterns of C. rogercresseyi have been addressed from data obtained from established monitoring and surveillance programs, studies on its spatial ecology are limited. A wide geographic distribution of C. rogercresseyi is presumed in Chile; however, how this species could potentially be distributed in space is unknown. Our study presents an analysis of the habitat suitability for C. rogercresseyi in the entire area occupied by marine sites of salmon farms in Chile. Habitat suitability modeling was used to explore the likelihood of species spatial occurrence based on environmental characteristics. Due to the expanding salmon industry in southern Chile, we studied C. rogercresseyi habitat suitability models for present (average of 2005-2010) and two future projections (2050 and 2100) under different climate change scenarios. Models were constructed with the maxent algorithm using a large database of spatial C. rogercresseyi occurrences from the Chilean fisheries health authority and included 23 environmental variables obtained from the Ocean Rasters for Analysis of Climate and Environment (Bio-ORACLE). Habitat suitability models indicated that water temperature, water salinity, and current velocity of waters were the most important characteristics limiting C. rogercresseyi distribution in southern Chile. Habitat suitability models for current climate indicated a heterogeneous pattern with C. rogercresseyi being present in waters with temperature range 12.12-7.08°C (sd = 0.65), salinity range 33.7-25.5 pss (sd = 1.73), and current water velocity range 0.23-0.01 m-1 (sd = 0.02). Predictions for future projections in year 2050 and year 2100 suggest new clumped dispersion of the environmental conditions for C. rogercresseyi establishment. Our results suggest complexity and a wide dispersion of the biogeographic distribution of the C. rogercresseyi habitat suitability with potential implications for control strategies and environmental issues for salmon farming in Chile. Further investigations are required into C. rogercresseyi distribution in southern Chile considering the possible effect of climate change.

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The joint and relative effects of future land-use and climate change on fire occurrence in the Amazon, as well its seasonal variation, are still poorly understood, despite its recognized importance. Using the maximum entropy method (MaxEnt), we combined regional land-use projections and climatic data from the CMIP5 multimodel ensemble to investigate the monthly probability of fire occurrence in the mid (2041-2070) and late (2071-2100) 21st century in the Brazilian Amazon. We found striking spatial variation in the fire relative probability (FRP) change along the months, with October showing the highest overall change. Considering climate only, the area with FRP ≥ 0.3 (a threshold chosen based on the literature) in October increases 6.9% by 2071-2100 compared to the baseline period under the representative concentration pathway (RCP) 4.5 and 27.7% under the RCP 8.5. The best-case land-use scenario ("Sustainability") alone causes a 10.6% increase in the area with FRP ≥ 0.3, while the worse-case land-use scenario ("Fragmentation") causes a 73.2% increase. The optimistic climate-land-use projection (Sustainability and RCP 4.5) causes a 21.3% increase in the area with FRP ≥ 0.3 in October by 2071-2100 compared to the baseline period. In contrast, the most pessimistic climate-land-use projection (Fragmentation and RCP 8.5) causes a widespread increase in FRP (113.5% increase in the area with FRP ≥ 0.3), and prolongs the fire season, displacing its peak. Combining the Sustainability land-use and RCP 8.5 scenarios causes a 39.1% increase in the area with FRP ≥ 0.3. We conclude that avoiding the regress on land-use governance in the Brazilian Amazon (i.e., decrease in the extension and level of conservation of the protected areas, reduced environmental laws enforcement, extensive road paving, and increased deforestation) would substantially mitigate the effects of climate change on fire probability, even under the most pessimistic RCP 8.5 scenario.

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Multiple-class land-cover classification approaches can be inefficient when the main goal is to classify only one or a few classes. Under this scenario one-class classification algorithms could be a more efficient alternative. Currently there are several algorithms that can fulfil this task, with MaxEnt being one of the most promising. However, there is scarce information regarding parametrization for performing land-cover classification using MaxEnt. In this study we aimed to understand how MaxEnt parameterization affects the classification accuracy of four different land-covers (i.e., built-up, irrigated grass, evergreen trees and deciduous trees) in the city of Santiago de Chile. We also evaluated if MaxEnt manual parameterization outperforms classification results obtained when using MaxEnt default parameters setting. To accomplish our objectives, we generated a set of 25,344 classification maps (i.e., 6,336 for each assessed land-cover), which are based on all the potential combination of 12 different classes of features restrictions, four regularization multipliers, four different sample sizes, three training/testing proportions, and 11 thresholds for generating the binary maps. Our results showed that with a good parameterization, MaxEnt can effectively classify different land covers with kappa values ranging from 0.68 for deciduous trees to 0.89 for irrigated grass. However, the accuracy of classification results is highly influenced by the type of land-cover being classified. Simpler models produced good classification outcomes for homogenous land-covers, but not for heterogeneous covers, where complex models provided better outcomes. In general, manual parameterization improves the accuracy of classification results, but this improvement will depend on the threshold used to generate the binary map. In fact, threshold selection showed to be the most relevant factor impacting the accuracy of the four land-cover classification. The number of sampling points for training the model also has a positive effect on classification results. However, this effect followed a logarithmic distribution, showing an improvement of kappa values when increasing the sampling from 40 to 60 points, but showing only a marginal effect if more than 60 sampling points are used. In light of these results, we suggest testing different parametrization and thresholds until satisfactory kappa or other accuracy metrics values are achieved. Our results highlight the huge potential that MaxEnt has a as a tool for one-class classification, but a good understanding of the software settings and model parameterization is needed to obtain reliable results.

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Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.

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A measure D [ t 1 , t 2 ] for the amount of dynamical evolution exhibited by a quantum system during a time interval [ t 1 , t 2 ] is defined in terms of how distinguishable from each other are, on average, the states of the system at different times. We investigate some properties of the measure D showing that, for increasing values of the interval's duration, the measure quickly reaches an asymptotic value given by the linear entropy of the energy distribution associated with the system's (pure) quantum state. This leads to the formulation of an entropic variational problem characterizing the quantum states that exhibit the largest amount of dynamical evolution under energy constraints given by the expectation value of the energy.

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Among the 13 Mico species recognized by the IUCN Red List of Threatened Species, six are listed as "Data Deficient". The geographic range of most of the Mico species has been estimated from only a few records. We report new localities and the geographic extension of Mico chrysoleucos. In addition, we confirmed the presence of the species in two distinct protected areas. We modeled the habitat suitability of M. chrysoleucos using the maximum entropy method and including new records obtained by the authors in the state of Amazonas, Brazil. From the total area of occurrence calculated for the species, 22.8% is covered by protected areas and indigenous lands. The annual mean deforestation rate estimated between 2000 and 2015 was 2.95%, and the total area deforested by 2015 was 3354 km2 or 8.6% of the total distribution limits of the species. The habitat lost between 2000 and 2015 was 3.2% (1131 km2) of the total potential distribution, while the habitat loss area legally protected was 31 km2, and the habitat loss in settlements was equal to 691 km2. Our results extend the geographic distribution of the species about 100 km farther south, with the Maracanã River being a possible geographic barrier for the species. The significantly low rate of habitat loss inside protected areas and indigenous land, when compared to unprotected areas, points out the importance of these areas to M. chrysoleucos conservation. The species is relatively wide-ranging, legally protected, and resilient to regional anthropic threats. However, the hydroelectric schemes and the improvement of the road system in southern Amazonia pose an imminent threat to the species.

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We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. To find the maximum entropy Markov chain, we use the thermodynamic formalism, which provides insightful connections with statistical physics and thermodynamics from which large deviations properties arise naturally. We provide an accessible introduction to the maximum entropy Markov chain inference problem and large deviations theory to the community of computational neuroscience, avoiding some technicalities while preserving the core ideas and intuitions. We review large deviations techniques useful in spike train statistics to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability, and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field.

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The 37th edition of MaxEnt was held in Brazil, hosting several distinguished researchers and students. The workshop offered four tutorials, nine invited talks, twenty four oral presentations and twenty seven poster presentations. All submissions received their first choice between oral and poster presentations. The event held a celebration to Julio Stern's 60th anniversary and awarded two prizes to young researchers. As customary, the workshop had one free afternoon, in which participants visited the city's surroundings and experienced Brazilian food and traditions.

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The spiking activity of neuronal networks follows laws that are not time-reversal symmetric; the notion of pre-synaptic and post-synaptic neurons, stimulus correlations and noise correlations have a clear time order. Therefore, a biologically realistic statistical model for the spiking activity should be able to capture some degree of time irreversibility. We use the thermodynamic formalism to build a framework in the context maximum entropy models to quantify the degree of time irreversibility, providing an explicit formula for the information entropy production of the inferred maximum entropy Markov chain. We provide examples to illustrate our results and discuss the importance of time irreversibility for modeling the spike train statistics.

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Risk neutral measures are defined such that the basic random assets in a portfolio are martingales. Hence, when the market model is complete, valuation of other financial instruments is a relatively straightforward task when those basic random assets constitute their underlying asset. To determine the risk neutral measure, it is assumed that the current prices of the basic assets are known exactly. However, oftentimes all we know about the current price, or that of a derivative having it as underlying, is a bid-ask range. The question then arises as to how to determine the risk neutral measure from that information. We may want to determine risk neutral measures from that information to use it, for example, to price other derivatives on the same asset. In this paper we propose an extended version of the maximum entropy method to carry out that task. This approach provides a novel solution to this problem, which is computationally simple and fast.

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ResumenLos especímenes silvestres de Vanilla planifolia G. Jack forman parte del acervo genético primario, los cuales solo se han reportado en Oaxaca, México. Por ello se evaluó la distribución de esta especie con el objetivo de ubicar y describir características ecológicas en zonas potenciales de distribución. La metodología empleada consistió de cuatro etapas: 1) Elaboración de una base de datos con registros de herbario;2) Construcción de la distribución potencial basado en los registros históricos de herbario para la especie, mediante el modelo de máxima entropía (Maxent), con el uso de 22 variables bioclimáticas como predictoras; 3) Realización de búsquedas sistemáticas de individuos in situ con base en los registros de herbario y las áreas de distribución potencial en 24 municipios, para conocer la situación y la distribución del hábitat actual, y 4) Descripción mediante factores ambientales de los nichos ecológicos potenciales generados por MaxEnt. La revisión de las colecciones de herbarios reportó un total de 18 registros de V. planifolia, comprendidos entre 1939 y 1998.La búsqueda sistemática de individuos en campo ubicó 28 plantas distribuidas en 12 sitios sobre 95 364 Km2. Las variables que contribuyeron con mayor valor porcentual para determinar la estimación del modelo de distribución potencial en vainilla son precipitación del periodo más lluvioso (61.9 %), régimen de humedad del suelo (23.4 %) y precipitación del cuatrimestre más lluvioso (8.1 %). El hábitat potencial de la especie se distribuyó en cuatro zonas; trópico húmedo del golfo de México, templado húmedo, trópico húmedo y templado húmedo del pacifico. La precipitación anual osciló de 2 500 a 4 000 mm, con lluvias en verano y porcentaje de precipitación invernal de 5 a 10 %. El régimen de humedad y clima predominantes fueron údico tipo I (330 a 365 días de humedad) y cálido húmedo (Am/A(C) m). Las plantas se ubicaron en altitudes de 200 a 1 190 msnm, en laderas accidentadas, que por lo general están al pie de sistemas montañosos de 1 300 a 2 500 metros de altitud. En condiciones naturales la distribución de la especie no se limita a selva alta perennifolia, dado que se ubicó en bosque mesófilo de montaña y bosque tropical perennifolio. La ubicación de nuevos especímenes de V. planifolia en condiciones silvestres reduce un 66 % del área potencial de distribución, y la fragmenta, al pasar de ser una zona continua a convertirse en tres zonas geográficamente separadas. La reducción del hábitat se debió a un aumento en el número de plantas ubicadas, lo que define las condiciones ambientales a un nivel más exacto. Por lo anterior, se pueden emprender o diseñar acciones de conservación enfocadas a áreas más específicas dentro del estado de Oaxaca, México.

AbstractWild specimens of Vanilla planifolia represent a vital part of this resource primary gene pool, and some plants have only been reported in Oaxaca, Mexico. For this reason, we studied its geographical distribution within the state, to locate and describe the ecological characteristics of the areas where they have been found, in order to identify potential areas of establishment. The method comprised four stages: 1) the creation of a database with herbarium records, 2) the construction of the potential distribution based on historical herbarium records for the species, using the model of maximum entropy (MaxEnt) and 22 bioclimatic variables as predictors; 3) an in situ systematic search of individuals, based on herbarium records and areas of potential distribution in 24 municipalities, to determine the habitat current situation and distribution; 4) the description of the environmental factors of potential ecological niches generated by MaxEnt. A review of herbarium collections revealed a total of 18 records of V. planifolia between 1939 and 1998. The systematic search located 28 plants distributed in 12 sites in 95 364 Km2. The most important variables that determined the model of vanilla potential distribution were: precipitation in the rainy season (61.9 %), soil moisture regime (23.4 %) and precipitation during the four months of highest rainfall (8.1 %). The species potential habitat was found to be distributed in four zones: wet tropics of the Gulf of Mexico, humid temperate, humid tropical, and humid temperate in the Pacific. Precipitation oscillated within the annual ranges of 2 500 to 4 000 mm, with summer rains, and winter precipitation as 5 to 10 % of the total. The moisture regime and predominating climate were udic type I (330 to 365 days of moisture) and hot humid (Am/A(C) m). The plants were located at altitudes of 200 to 1 190 masl, on rough hillsides that generally make up the foothills of mountain systems, with altitudes of 1 300 to 2 500 masl. In natural conditions, distribution of the species is not limited to high evergreen forests, since it was also found in mountain mesophyll and tropical evergreen forests. The location of new specimens of V. planifolia in its wild condition reduces the potential distribution area by 66 %. This area is fragmented into three geographically separated areas. Habitat reduction was due to the increased number of located plants that define the environmental conditions into a more accurate level. Conservation actions can thus be designed and implemented, focusing on more specific areas within the state of Oaxaca, Mexico.

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Abstract Recently, ecological niche models have been employed to investigate the potential geographical distribution of species. However, it is necessary to analyze the vast number of publications on this topic to understand the trends and biases of research using ecological niche models (ENMs). Therefore, this study aims to investigate trends in the scientific literature regarding studies on ENMs. For the quantitative analysis of the literature on ENMs, we performed a search in the Thomson ISI (Web of Science) database between 1991 and 2013. The search identified 3042 papers containing preselected keywords in either the title or abstract. The results showed that the number of papers has increased over the years (r=0.77, P<0.001), with a sharp increase in recent years, highlighting the widespread use of the ENMs. There was an increase in the diversity of journals that published papers about ENMs (r=0.97, P<0.001). The research was conducted in different countries, predominantly the United States of America (550 papers), and the most commonly used method was the Maximum Entropy method (312 papers). Regarding the taxonomic group, most research has been conducted on plants (402 papers, or 28.36% of the total). There was no relationship between the modeling method used and the taxonomic group studied (χ2=4.8, P=0.15). Finally, the wide availability of biological, environmental and computational resources has elicited the broad use of tools for ENMs. Despite the conceptual discussions of the ENMs, this method is currently the most effective way to evaluate the potential geographical distribution of species, and to predict the distribution under different environmental conditions (i.e., future or past scenarios).

Resumo Recentemente, modelos de nicho têm sido empregados para investigar a distribuição geográfica potencial de espécies. Porém, é necessário analisar a vasta quantidade de publicações sobre o referido tema, a fim de compreender as tendências e vieses das pesquisas que usam modelos de nicho ecológico (MNEs). Portanto, esse trabalho tem por objetivo investigar as tendências da literatura científica de trabalhos sobre MNEs. Para a análise quantitativa das publicações sobre MNEs, foi realizada uma busca na base de dados Thomson ISI (Web of Science), entre o período de 1991 a 2013. A pesquisa agrupou 3042 documentos que continham nas palavras-chave, no título ou no resumo os termos selecionados para a busca. Os resultados mostraram que de forma geral o número de artigos tem aumentado ao longo dos anos (r=0,77, P<0,001), com um acentuado crescimento nos anos mais recentes, destacando o amplo uso de MNEs. Ao longo dos anos percebeu-se um aumento da diversidade de revistas que publicam sobre o assunto (r=0,97, P<0,001). As pesquisas têm sido desenvolvidas em diferentes países, com predomínio dos Estados Unidos (550 artigos) e o método mais utilizado foi o de Máxima Entropia (312 artigos). Quanto ao grupo taxonômico, a maioria dos estudos tem ocorrido com plantas (402 artigos ou 28,36% do total de artigos). Não houve relação entre método de modelagem e grupo taxonômico (χ2=4,8, P=0,15). Por fim, a ampla disponibilidade de dados biológicos, ambientais e recursos computacionais tem propiciado um amplo uso de MNEs. Apesar das discussões conceituais sobre MNEs, o método atualmente é o mais eficaz para conhecer a distribuição geográfica potencial das espécies e ainda projetar essa distribuição sob diferentes condições ambientais (i.e. cenários futuros, passado).

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