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      Inferring space from time: On the relationship between demography and environmental suitability in the desert plant O. rastrera

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          Abstract

          Demographic analyses and ecological niche modeling (ENM) are two popular tools that address species persistence in relation to environmental conditions. Classic demography provides detailed information about the mechanisms that allow a population to grow or remain stable at a local scale, while ENM infers distributions from conditions suitable for species persistence at geographic scales by relating species’ occurrences with environmental variables. By integrating these two tools, we may better understand population processes that determine species persistence at a geographic scale. To test this idea, we developed a model that relates climate to demography of the cactus Opuntia rastrera using 15 years of data from one locality. Using this model we determined the geographic area where populations would have positive growth rates given its climatic conditions. The climate-dependent demographic model showed poor performance as a distribution model, but it was helpful in defining some mechanisms that determine species’ distributions. For instance, high rainfall had a negative impact on the population growth rate by increasing mortality. Rainy areas to the west of the distribution of O. rastrera were identified as unsuitable both by our climate-dependent demographic model and by a popular ENM algorithm (MaxEnt), suggesting that distribution is constrained by excessive rains due to high mortality. Areas projected to be climatically suitable by MaxEnt were not related with higher population growth rates. Instead, we found a strong correlation between environmental distance to the niche centroid (center of the niche hypervolume, where optimal conditions may occur) and population growth rate, meaning that the niche centroid approach is helpful in finding high-fitness areas.

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          Most cited references 29

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          Niches and distributional areas: concepts, methods, and assumptions.

          Estimating actual and potential areas of distribution of species via ecological niche modeling has become a very active field of research, yet important conceptual issues in this field remain confused. We argue that conceptual clarity is enhanced by adopting restricted definitions of "niche" that enable operational definitions of basic concepts like fundamental, potential, and realized niches and potential and actual distributional areas. We apply these definitions to the question of niche conservatism, addressing what it is that is conserved and showing with a quantitative example how niche change can be measured. In this example, we display the extremely irregular structure of niche space, arguing that it is an important factor in understanding niche evolution. Many cases of apparently successful models of distributions ignore biotic factors: we suggest explanations to account for this paradox. Finally, relating the probability of observing a species to ecological factors, we address the issue of what objects are actually calculated by different niche modeling algorithms and stress the fact that methods that use only presence data calculate very different quantities than methods that use absence data. We conclude that the results of niche modeling exercises can be interpreted much better if the ecological and mathematical assumptions of the modeling process are made explicit.
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            Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria.

            Maxent, one of the most commonly used methods for inferring species distributions and environmental tolerances from occurrence data, allows users to fit models of arbitrary complexity. Model complexity is typically constrained via a process known as L1 regularization, but at present little guidance is available for setting the appropriate level of regularization, and the effects of inappropriately complex or simple models are largely unknown. In this study, we demonstrate the use of information criterion approaches to setting regularization in Maxent, and we compare models selected using information criteria to models selected using other criteria that are common in the literature. We evaluate model performance using occurrence data generated from a known "true" initial Maxent model, using several different metrics for model quality and transferability. We demonstrate that models that are inappropriately complex or inappropriately simple show reduced ability to infer habitat quality, reduced ability to infer the relative importance of variables in constraining species' distributions, and reduced transferability to other time periods. We also demonstrate that information criteria may offer significant advantages over the methods commonly used in the literature.
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              Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models.

              Species responses to climate change may be influenced by changes in available habitat, as well as population processes, species interactions and interactions between demographic and landscape dynamics. Current methods for assessing these responses fail to provide an integrated view of these influences because they deal with habitat change or population dynamics, but rarely both. In this study, we linked a time series of habitat suitability models with spatially explicit stochastic population models to explore factors that influence the viability of plant species populations under stable and changing climate scenarios in South African fynbos, a global biodiversity hot spot. Results indicate that complex interactions between life history, disturbance regime and distribution pattern mediate species extinction risks under climate change. Our novel mechanistic approach allows more complete and direct appraisal of future biotic responses than do static bioclimatic habitat modelling approaches, and will ultimately support development of more effective conservation strategies to mitigate biodiversity losses due to climate change.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: Project administrationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                9 August 2018
                2018
                : 13
                : 8
                Affiliations
                [1 ] Departamento de Ecología y Recursos Naturales, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
                [2 ] Departamento de Zoología, Instituto de Biología, Universidad Nacional Autónoma de México, Mexico City, Mexico
                [3 ] Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO), Insurgentes Sur-Periférico, Tlalpan Mexico City, Mexico
                [4 ] Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Mexico City, Mexico
                Universita degli Studi di Napoli Federico II, ITALY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                [¤]

                Current address: Laboratorio de Genética Molecular, Desarrollo y Evolución de Plantas, Instituto de Ecología, Universidad Nacional Autónoma de México, Mexico City, Mexico

                Article
                PONE-D-18-02312
                10.1371/journal.pone.0201543
                6084933
                30092077
                © 2018 Ureta et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 4, Tables: 1, Pages: 20
                Product
                Funding
                The authors received no specific funding for this work. However; the first and corresponding author had a posdoctoral scholartship by DGAPA UNAM during the study period.
                Categories
                Research Article
                Biology and Life Sciences
                Ecology
                Ecological Niches
                Ecology and Environmental Sciences
                Ecology
                Ecological Niches
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Population Growth
                Earth Sciences
                Atmospheric Science
                Climatology
                Climate Modeling
                Earth Sciences
                Atmospheric Science
                Meteorology
                Rain
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Cloning
                Research and Analysis Methods
                Molecular Biology Techniques
                Cloning
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Fecundity
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Mathematical Models
                Biology and Life Sciences
                Population Biology
                Population Dynamics
                Custom metadata
                All data underlying the study are within the paper and its Supporting Information files.

                Uncategorized

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