scispace - formally typeset
Search or ask a question
Author

Víctor Sánchez-Cordero

Other affiliations: University of Kansas
Bio: Víctor Sánchez-Cordero is an academic researcher from National Autonomous University of Mexico. The author has contributed to research in topics: Population & Ecological niche. The author has an hindex of 37, co-authored 125 publications receiving 5864 citations. Previous affiliations of Víctor Sánchez-Cordero include University of Kansas.


Papers
More filters
Journal ArticleDOI
20 Aug 1999-Science
TL;DR: Reciprocal geographic predictions based on ecological niche models of sister taxon pairs of birds, mammals, and butterflies in southern Mexico indicate niche conservatism over several million years of independent evolution but little conservatism at the level of families.
Abstract: Theory predicts low niche differentiation between species over evolutionary time scales, but little empirical evidence is available. Reciprocal geographic predictions based on ecological niche models of sister taxon pairs of birds, mammals, and butterflies in southern Mexico indicate niche conservatism over several million years of independent evolution (between putative sister taxon pairs) but little conservatism at the level of families. Niche conservatism over such time scales indicates that speciation takes place in geographic, not ecological, dimensions and that ecological differences evolve later.

1,380 citations

Journal ArticleDOI
11 Apr 2002-Nature
TL;DR: Using a genetic algorithm and museum specimen occurrence data, ecological niche models are developed for 1,870 species occurring in Mexico and projected onto two climate surfaces modelled for 2055, suggesting that severe ecological perturbations may result.
Abstract: Global climates are changing rapidly, with unexpected consequences. Because elements of biodiversity respond intimately to climate as an important driving force of distributional limitation, distributional shifts and biodiversity losses are expected. Nevertheless, in spite of modelling efforts focused on single species or entire ecosystems, a few preliminary surveys of fauna-wide effects, and evidence of climate change-mediated shifts in several species, the likely effects of climate change on species' distributions remain little known, and fauna-wide or community-level effects are almost completely unexplored. Here, using a genetic algorithm and museum specimen occurrence data, we develop ecological niche models for 1,870 species occurring in Mexico and project them onto two climate surfaces modelled for 2055. Although extinctions and drastic range reductions are predicted to be relatively few, species turnover in some local communities is predicted to be high (>40% of species), suggesting that severe ecological perturbations may result.

854 citations

Journal ArticleDOI
TL;DR: It is predicted that climate change will exacerbate the ecological risk of human exposure to leishmaniasis in areas outside its present range in the United States and, possibly, in parts of southern Canada.
Abstract: Background Climate change is increasingly being implicated in species' range shifts throughout the world, including those of important vector and reservoir species for infectious diseases. In North America (Mexico, United States, and Canada), leishmaniasis is a vector-borne disease that is autochthonous in Mexico and Texas and has begun to expand its range northward. Further expansion to the north may be facilitated by climate change as more habitat becomes suitable for vector and reservoir species for leishmaniasis. Methods and Findings The analysis began with the construction of ecological niche models using a maximum entropy algorithm for the distribution of two sand fly vector species (Lutzomyia anthophora and L. diabolica), three confirmed rodent reservoir species (Neotoma albigula, N. floridana, and N. micropus), and one potential rodent reservoir species (N. mexicana) for leishmaniasis in northern Mexico and the United States. As input, these models used species' occurrence records with topographic and climatic parameters as explanatory variables. Models were tested for their ability to predict correctly both a specified fraction of occurrence points set aside for this purpose and occurrence points from an independently derived data set. These models were refined to obtain predicted species' geographical distributions under increasingly strict assumptions about the ability of a species to disperse to suitable habitat and to persist in it, as modulated by its ecological suitability. Models successful at predictions were fitted to the extreme A2 and relatively conservative B2 projected climate scenarios for 2020, 2050, and 2080 using publicly available interpolated climate data from the Third Intergovernmental Panel on Climate Change Assessment Report. Further analyses included estimation of the projected human population that could potentially be exposed to leishmaniasis in 2020, 2050, and 2080 under the A2 and B2 scenarios. All confirmed vector and reservoir species will see an expansion of their potential range towards the north. Thus, leishmaniasis has the potential to expand northwards from Mexico and the southern United States. In the eastern United States its spread is predicted to be limited by the range of L. diabolica; further west, L. anthophora may play the same role. In the east it may even reach the southern boundary of Canada. The risk of spread is greater for the A2 scenario than for the B2 scenario. Even in the latter case, with restrictive (contiguous) models for dispersal of vector and reservoir species, and limiting vector and reservoir species occupancy to only the top 10% of their potential suitable habitat, the expected number of human individuals exposed to leishmaniasis by 2080 will at least double its present value. Conclusions These models predict that climate change will exacerbate the ecological risk of human exposure to leishmaniasis in areas outside its present range in the United States and, possibly, in parts of southern Canada. This prediction suggests the adoption of measures such as surveillance for leishmaniasis north of Texas as disease cases spread northwards. Potential vector and reservoir control strategies—besides direct intervention in disease cases—should also be further investigated.

288 citations

Journal ArticleDOI
TL;DR: This tool was used to identify host relationships of Triatoma species implicated in transmission of Chagas disease, and species pairs that were identified coincided exactly with those in previous studies, suggesting that local interactions between Triatomas and Neotoma species and subspecies have implications at a geographic level.
Abstract: Ecologic niche modeling may improve our understanding of epidemiologically relevant vector and parasitereservoir distributions. We used this tool to identify host relationships of Triatoma species implicated in transmission of Chagas disease. Associations have been documented between the protracta complex (Triatoma: Triatominae: Reduviidae) with packrat species (Neotoma spp.), providing an excellent case study for the broader challenge of developing hypotheses of association. Species pairs that were identified coincided exactly with those in previous studies, suggesting that local interactions between Triatoma and Neotoma species and subspecies have implications at a geographic level. Nothing is known about sylvatic associates of T. barberi, which are considered the primary Chagas vector in Mexico; its geographic distribution coincided closely with that of N. mexicana, suggesting interaction. The presence of this species was confirmed in two regions where it had been predicted but not previously collected. This approach may help in identifying Chagas disease risk areas, planning vector-control strategies, and exploring parasite-reservoir associations for other emerging diseases. hagas disease is caused by the parasitic protozoan Trypanosoma cruzi and transmitted by blood-feeding insects in the family Reduviidae, subfamily Triatominae. Chagas disease is an important cause of illness and death throughout the Americas, affecting 16–18 million persons. While an estimated 100 million persons in 21 countries in the New World live in endemic areas and are at risk for infection, the disease is principally a zoonotic infection, in which sylvatic mammals serve as reservoir hosts and zoophilic triatomine species as vectors. The protracta species group consists of seven species (Triatoma protracta, T. peninsularis, T. sinaloensis, T. neotomae, T. barberi, T. nitida, and T. incrassata); T. protracta contains five subspecies: T. p. protracta, T. p. woodi, T. p. navajoensis, T. p. zacatecensis, and T. p. nahuatlae (1,2). This group is restricted to the southwestern United States and Mexico. Previous studies have demonstrated high host specificity in this species group, involving woodrats or packrats (Neotoma spp.) (1). Whereas host associations of Triatoma are often complex, the protracta group shows remarkable host specificity and geographic distributions suggestive of host-ectoparasite cospeciation. A new tool in the study of geographic phenomena in ecology and systematics is ecologic niche modeling of primary occurrence data (data placing a particular species in a particular site) (3). In general, the approach involves a machine-learning algorithm for discovering associations between pointoccurrence data and sets of electronic maps summarizing environmental/ecologic dimensions that may or may not be important in limiting species’ geographic distributions. These associations constitute an approximation of species’ fundamental ecologic niches (the conjunction of ecologic conditions in which a species is able to maintain populations without immigration) (4) and hence provide a basis for understanding numerous ecologic and geographic phenomena related to species distributions. We applied ecologic niche modeling to identify host relationships of Triatoma species and subspecies implicated in the transmission of Chagas disease. Previous studies by Ryckman (1) provide an ideal test case: hypotheses of association developed based on the modeling approach can be tested independently by using associations identified in Ryckman’s detailed field studies. If successful, this approach would be invaluable in identifying host relationships for species for which detailed information is not available, for stratifying Chagas disease risk areas, and for planning the operational aspects of vector control strategies. Methods

225 citations

Journal ArticleDOI
TL;DR: In this paper, a cross-species analysis of the effects of global climate change on the distributions of one bird family, the Cracidae, in Mexico, based on projecting models of ecological niches from present conditions to modeled future conditions taken from general circulation models of climate change.

222 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.

13,120 citations

Journal ArticleDOI
08 Jan 2004-Nature
TL;DR: Estimates of extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.
Abstract: Climate change over the past approximately 30 years has produced numerous shifts in the distributions and abundances of species and has been implicated in one species-level extinction. Using projections of species' distributions for future climate scenarios, we assess extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface. Exploring three approaches in which the estimated probability of extinction shows a power-law relationship with geographical range size, we predict, on the basis of mid-range climate-warming scenarios for 2050, that 15-37% of species in our sample of regions and taxa will be 'committed to extinction'. When the average of the three methods and two dispersal scenarios is taken, minimal climate-warming scenarios produce lower projections of species committed to extinction ( approximately 18%) than mid-range ( approximately 24%) and maximum-change ( approximately 35%) scenarios. These estimates show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.

7,089 citations

Journal ArticleDOI
TL;DR: An overview of recent advances in species distribution models, and new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales are suggested.
Abstract: In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.

5,620 citations

01 Jan 1980
TL;DR: In this article, the influence of diet on the distribution of nitrogen isotopes in animals was investigated by analyzing animals grown in the laboratory on diets of constant nitrogen isotopic composition and found that the variability of the relationship between the δ^(15)N values of animals and their diets is greater for different individuals raised on the same diet than for the same species raised on different diets.
Abstract: The influence of diet on the distribution of nitrogen isotopes in animals was investigated by analyzing animals grown in the laboratory on diets of constant nitrogen isotopic composition. The isotopic composition of the nitrogen in an animal reflects the nitrogen isotopic composition of its diet. The δ^(15)N values of the whole bodies of animals are usually more positive than those of their diets. Different individuals of a species raised on the same diet can have significantly different δ^(15)N values. The variability of the relationship between the δ^(15)N values of animals and their diets is greater for different species raised on the same diet than for the same species raised on different diets. Different tissues of mice are also enriched in ^(15)N relative to the diet, with the difference between the δ^(15)N values of a tissue and the diet depending on both the kind of tissue and the diet involved. The δ^(15)N values of collagen and chitin, biochemical components that are often preserved in fossil animal remains, are also related to the δ^(15)N value of the diet. The dependence of the δ^(15)N values of whole animals and their tissues and biochemical components on the δ^(15)N value of diet indicates that the isotopic composition of animal nitrogen can be used to obtain information about an animal's diet if its potential food sources had different δ^(15)N values. The nitrogen isotopic method of dietary analysis probably can be used to estimate the relative use of legumes vs non-legumes or of aquatic vs terrestrial organisms as food sources for extant and fossil animals. However, the method probably will not be applicable in those modern ecosystems in which the use of chemical fertilizers has influenced the distribution of nitrogen isotopes in food sources. The isotopic method of dietary analysis was used to reconstruct changes in the diet of the human population that occupied the Tehuacan Valley of Mexico over a 7000 yr span. Variations in the δ^(15)C and δ^(15)N values of bone collagen suggest that C_4 and/or CAM plants (presumably mostly corn) and legumes (presumably mostly beans) were introduced into the diet much earlier than suggested by conventional archaeological analysis.

5,548 citations

Journal ArticleDOI
TL;DR: This paper presents a tuning method that uses presence-only data for parameter tuning, and introduces several concepts that improve the predictive accuracy and running time of Maxent and describes a new logistic output format that gives an estimate of probability of presence.
Abstract: 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 presence-absence 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 performance; 3) logistic output improves model calibration, so that large differences in output values correspond better to large differences in suitability; 4) "target-group" background sampling can give much better predictive performance than random background sampling; 5) random background sampling results in a dramatic decrease in running time, with no decrease in model performance.

5,314 citations