A Dynamic Theory of the Area of Distribution

Aims To propose and analyze a general, dynamic, process-oriented theory of the area of distribution. Methods The area of distribution is modelled by combining (by multiplication) three matrices: one matrix represents movements, another niche tolerances, and a third, biotic interactions. Results are derived from general properties of this product and from simulation of a cellular automaton defined in terms of the matrix operations. Everything is implemented practically in an R package. Results Results are obtained by simulation and by mathematical analysis. We show that the mid-domain effect is a direct consequence of dispersal; that to include movements to Ecological Niche Modeling significantly affects results, but cannot be done without choosing an ancestral area of distribution. We discuss ways of estimating such ancestral areas. We show that, in our approach, movements and niche effects are mixed in ways almost impossible to disentangle, and show this is a consequence of the singularity of a matrix. We introduce a tool (the Connectivity-Suitability-Dispersal plot) to extend the results of simple niche modeling to understand the effects of dispersal. Main conclusions The conceptually straightforward scheme we present for the area of distribution integrates, in a mathematically sound and computationally feasible way, several key ideas in biogeography: the geographic and environmental matrix, the Grinnellian niche, dispersal capacity and the ancestral area of origin of groups of species. We show that although full simulations are indispensable to obtain the dynamics of an area of distribution, interesting results can be derived simply by analyzing the matrices representing the dynamics.Comment: 45 pages including appendixes, 12 figures, submitted to Journal of Biogeograph

Co-occurrence Networks do not Support Identification of Biotic Interactions

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.We assess a body of work that has attempted to use co-occurrence networks to infer the existence and type of biotic interactions between species. Although we see considerable interest in the approach as an exploratory tool for understanding patterns of co-occurrence of species, we note and describe numerous problems in the step of inferring biotic interactions from the co-occurrence patterns. These problems are both theoretical and empirical in nature, and limit confidence in inferences about interactions rather severely. We examine a series of examples that demonstrates striking discords between interactions inferred from co-occurrence patterns and previous experimental results and known life-history details

Response to Stephens et al. (2019)

Rebuttal to Stephens et al. (2019), as part of a debate format

Response to Stephens et al. (2019)

Rebuttal to Stephens et al. (2019), as part of a debate format

Kuenm: An R package for detailed development of ecological niche models using Maxent

This work is licensed under a Creative Commons Attribution 4.0 International License.Background Ecological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a challenging task. The calibration phase of these models is critical, but despite recent attempts at providing tools for performing this step, adequate detail is still missing. Here, we present the kuenm R package, a new set of tools for performing detailed development of ecological niche models using the platform Maxent in a reproducible way. Results This package takes advantage of the versatility of R and Maxent to enable detailed model calibration and selection, final model creation and evaluation, and extrapolation risk analysis. Best parameters for modeling are selected considering (1) statistical significance, (2) predictive power, and (3) model complexity. For final models, we enable multiple parameter sets and model transfers, making processing simpler. Users can also evaluate extrapolation risk in model transfers via mobility-oriented parity (MOP) metric. Discussion Use of this package allows robust processes of model calibration, facilitating creation of final models based on model significance, performance, and simplicity. Model transfers to multiple scenarios, also facilitated in this package, significantly reduce time invested in performing these tasks. Finally, efficient assessments of strict-extrapolation risks in model transfers via the MOP and MESS metrics help to prevent overinterpretation in model outcomes.PAPIIT UNAM IN116018CONACyT-FORDECyT 27364

Insufficient protection and intense human pressure threaten islands worldwide

The small size, degree of isolation, and particular evolutionary processes occurring in islands make them highly diverse and an essential target for conservation. For the same characteristics, they are also extremely vulnerable to human-mediated disturbances. During the last centuries, nearly 80% of species extinctions have occurred on islands. While there is information on the human threats, level of protection, and conservation importance of islands, an integrative picture combining these aspects and aimed at determining conservation priorities to inform decision-making is still missing. Here, we jointly analyzed these three aspects producing a worldwide island conservation assessment based on terrestrial vertebrates. Considering the Aichi target of >17% of protection and all protected area categories, we found that 5397 islands, encompassing a quarter of the worldwide island area, face high human modification and have a low level of protection, with 33% of them showing extreme levels of human modification. Also, if we were to consider the new threshold of protected area coverage proposed to accurately protect the world's biodiversity and ecosystems (Nature Needs Half initiative), 77% of the world's islands would face this dramatic scenario. Furthermore, most large islands harboring the highest number of threatened vertebrates are found on this critical situation (low protection and high human modification). Based on the analysis of these conservation scenarios, we identified potential priority islands that provide opportunities to improve island conservation worldwide. The mbest opportunities are located in 58 islands with a low level of protection and human modification, which harbor more than 5 threatened vertebrates’ species and are located in different regions of the world.Fil: Nori, Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Diversidad y Ecología Animal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Diversidad y Ecología Animal; ArgentinaFil: Villalobos Camacho, Crisoforo Fabricio. Consejo Nacional de Ciencia y Tecnología. Instituto de Ecología; MéxicoFil: Osorio Olvera, Luis Alfredo. Universidad Nacional Autónoma de México; MéxicoFil: Loyola, Rafael. Universidade Federal de Goiás; Brasi

A comment on “Species are not most abundant in the centre of their geographic range or climatic niche”

A study published recently argued against a relationship between population density and position in geographic and environmental spaces. We found a number of methodological problems underlying the analysis. We discuss the main issues and conclude that these problems hinder a robust conclusion about the original question

Diferencias conceptuales entre modelación de nichos y modelación de áreasde distribución

The ideas of modeling niches of species and modeling areas of distribution, for some reason are confused terminologically. The terms " Species Distribution Models (SDM), and" Ecological Niche Models "(ENM) are very often considered synonymous, leaving aside the purely semantic problem, the modeling of niches and the modeling of distribution areas are strictly related activities. but clearly distinct, in this contribution we clarify these differences, presenting some examples

Complex groundwater flow systems as traveling agent models

Analyzing field data from pumping tests, we show that as with many other natural phenomena, groundwater flow exhibits a complex dynamics described by 1/f power spectrum. This result is theoretically studied within an agent perspective. Using a traveling agent model, we prove that this statistical behavior emerges when the medium is complex. Some heuristic reasoning is provided to justify both spatial and dynamic complexity, as the result of the superposition of an infinite number of stochastic processes. Even more, we show that this implies that non-Kolmogorovian probability is needed for its study, and provide a set of new partial differential equations for groundwater flow.Comment: 24 pages, 3 figure