Comparison of four modeling tools for the prediction of potential distribution for non-indigenous weeds in the United States

The expansion process of the Iberian ibex in the Sierra de Guadarrama National Park, Madrid (Spain)

In this paper we explore the usefulness of MaxEnt to predict the most suitable areas for a wildlife species, the Iberian ibex (Capra pyrenaica). For two decades (1990–2010), the species was established in a small part of the National Park Sierra de Guadarrama (Spain) and there has been a process of expansion to other areas of this protected area since 2010. However, almost two decades have elapsed since the modeling methods (MaxEnt) were proposed and no studies have tested their effectiveness using real distribution data, i.e. data from past predictions, to see if they fit the current distribution. We generated a model with presence– only data from 2007 and verified accuracy from 2017 data concerning real presence. Our results show a relationship between models and the species' current presence. The generated model can be useful to define the preferred locations of the species. We detected several differences between males and females of the species. This work not only shows the importance of selecting climatic and ecological variables for the construction of models but also indicates that they must be adjusted, at least for some species, to each sex and period of the year.

The risk of forfeiting the ranges of reptiles under nonrandom and stochastic scenarios of moving climate conditions: a case study for 115 species in China

Revealing the hazard features of forfeiting areal ranges for nonidentical scenarios of shifting climatic conditions is pivotal for the conformation of reptiles to climatic warming. Taking 115 reptiles in China as an example, the indefiniteness and danger of shrinking geographical range for the reptiles under stochastic and nonrandom scenarios of moving climatic situations were inspected via exploiting the scenarios of shifting climatic status associated with the representative concentration pathways, Monte Carlo simulation, and the classifications scheme based on the fuzzy set. For non-stochastic states of altering climatic elements, the richness of 115 reptiles improved in certain sites of northeastern, and western China and dropped in several areas of northern, eastern, central China, and southeastern China: roughly 59-74 reptiles forfeiting less than 20% of their present ranges, roughly 25-34 reptiles narrowing less than 20-40% of their present areal ranges, and roughly 105-111 reptiles inhabited more than 80% of their overall areal ranges. For the random status of shifting climatic elements, the count of reptiles that forfeited the various extent of the present or entire areal ranges descended with raising the eventuality; with a possibility of over 0.6, the count of reptiles that minified less than 20%, 20-40%, 40-60%, 60-80% and over 80% of the present ranges was roughly 28-49, 5-10, 1-3, 0-1 and 13-18, separately; the count of reptiles that inhabited below 20%, 20-40%, 40-60%, 60-80% and more than 80% of the entire real ranges was roughly 0-1, 5-6, 1-5, 0-2 and 35-36, separately. About 30% of 115 reptiles would face disappearance danger in response to moving climate conditions in the absence of adaption steps, and the conformation measures were indispensable for the reptiles that shrunk their areas.

Transferability of correlative and process-based species distribution models revisited: A response to Booth

Here, we respond to Booth's criticism of our paper, "Predictive ability of a process-based versus a correlative species distribution model." Booth argues that our usage of the MaxEnt model was flawed and that the conclusions of our paper are by implication flawed. We respond by clarifying that the error Booth implies we made was not made in our analysis, and we repeat statements from the original manuscript which anticipated such criticisms. In addition, we illustrate that using BIOCLIM variables in a MaxEnt analysis as recommended by Booth does not change the conclusions of the original analysis. That is, high performance in the training data domain did not equate to reliable predictions in novel data domains, and the process model transferred into novel data domains better than the correlative model did. We conclude by discussing a hidden implication of our study, namely, that process-based SDMs negate the need for BIOCLIM-type variables and therefore reframe the variable selection problem in species distribution modeling.

Spatio-Temporal Distribution of Digitaria insularis: Risk Analysis of Areas with Potential for Selection of Glyphosate-Resistant Biotypes in Eucalyptus Crops in Brazil

The objective of this study was to model the spatio-temporal distribution of Digitaria insularis (D. insularis) and analyze the risk of selection of glyphosate-resistant biotypes in eucalyptus cultivation in Brazil. Global data on the distribution of the specie were collected and associated with their ideal growth characteristics. The models were generated using Climex software, providing a predictive modeling technique. Biological data, species distribution, and climatic parameters were used to predict and map potential areas for the species of interest through the combination of growth and stress indices, giving rise to the Ecoclimatic Index (EI). The spatial distribution of D. insularis is predominantly in South and Central America and southern North America. The model had a good fit with the collected data and predicted higher EI values for tropical and subtropical regions, as was the case in Brazil. Species growth can occur throughout the year, with lower rates in winter, mainly in the country’s southern regions. Brazil has high climatic suitability for the occurrence of Digitaria insularis. Due to the climate suitability evidenced by the models and the expressive use of the same active ingredient, there is a risk of selecting glyphosate-resistant Digitaria insularis biotypes in eucalyptus cultivation areas.

Predictive ability of a process-based versus a correlative species distribution model

Species distribution modeling is a widely used tool in many branches of ecology and evolution. Evaluations of the transferability of species distribution models-their ability to predict the distribution of species in independent data domains-are, however, rare. In this study, we contrast the transferability of a process-based and a correlative species distribution model. Our case study uses 664 Australian eucalypt and acacia species. We estimate models for these species using data from their native Australia and then assess whether these models can predict the adventive range of these species. We find that the correlative model-MaxEnt-has a superior ability to describe the data in the training data domain (Australia) and that the process-based model-TTR-SDM-has a superior ability to predict the distribution of the study species outside of Australia. The implication of this analysis, that process-based models may be more appropriate than correlative models when making projections outside of the domain of the training data, needs to be tested in other case studies.

Instituting a national early detection and rapid response program: needs for building federal risk screening capacity

The invasive species issue is inherently a matter of risk; what is the risk that an invasive species will adversely impact valued assets? The early detection of and rapid response to invasive species (EDRR) requires that an assessment of risk is conducted as rapidly as possible. We define risk screening as rapid characterization of the types and degree of risks posed by a population of non-native species in a particular spatio-temporal context. Risk screening is used to evaluate the degree to which various response measures are warranted and justifiable. In this paper, we evaluate the US government’s risk screening programs with a view towards advancing national EDRR capacity. Our survey-based findings, consistent with prior analyses, indicate that risk evaluation by federal agencies has largely been a reactive, ad hoc process, and there is a need to improve information sharing, risk evaluation tools, and staff capacity for risk screening. We provide an overview of the US Department of Agriculture’s Tiered Weed Risk Evaluation and US Fish and Wildlife Service’s Ecological Risk Screening Summaries, two relatively new approaches to invasive species risk screening that hold promise as the basis for future work. We emphasize the need for a clearinghouse of risk evaluation protocols, tools, completed assessments and associated information; development of performance metrics and standardized protocols for risk screening; as well as support for complementary, science-based tools to facilitate and validate risk screening.

Prediction of the Long-Term Potential Distribution of Cryptorhynchus lapathi (L.) under Climate Change

The poplar and willow borer, Cryptorhynchus lapathi (L.), is a severe worldwide quarantine pest that causes great economic, social, and ecological damage in Europe, North America, and Asia. CLIMEX4.0.0 was used to study the likely impact of climate change on the potential global distribution of C. lapathi based on existing (1987–2016) and predicted (2021–2040, 2041–2080, and 2081–2100) climate data. Future climate data were simulated based on global climate models from Coupled Model Inter-comparison Project Phase 5 (CMIP5) under the RCP4.5 projection. The potential distribution of C. lapathi under historical climate conditions mainly includes North America, Africa, Europe, and Asia. Future global warming may cause a northward shift in the northern boundary of potential distribution. The total suitable area would increase by 2080–2100. Additionally, climatic suitability would change in large regions of the northern hemisphere and decrease in a small region of the southern hemisphere. The projected potential distribution will help determine the impacts of climate change and identify areas at risk of pest invasion in the future. In turn, this will help design and implement effective prevention measures for expanding pest populations, using natural enemies, microorganisms, and physical barriers in very favorable regions to impede the movement and oviposition of C. lapathi.