Blacklegged ticks (Ixodes scapularis Say) pose an enormous public health risk in eastern North America as the vector responsible for transmitting 7 human pathogens, including those causing the most common vector-borne disease in the United States, Lyme disease. Species distribution modeling is an increasingly popular method for predicting the potential distribution and subsequent risk of blacklegged ticks, however, the development of such models thus far is highly variable and would benefit from the use of standardized protocols. To identify where standardized protocols would most benefit current distribution models, we completed the \"Overview, Data, Model, Assessment, and Prediction\" (ODMAP) distribution modeling protocol for 21 publications reporting 22 blacklegged tick distribution models. We calculated an average adherence of 73.4% (SD ± 29%). Most prominently, we found that authors could better justify and connect their selection of variables and associated spatial scales to blacklegged tick ecology. In addition, the authors could provide clearer descriptions of model development, including checks for multicollinearity, spatial autocorrelation, and plausibility. Finally, authors could improve their reporting of variable effects to avoid undermining the models\' utility in informing species-environment relationships. To enhance future model rigor and reproducibility, we recommend utilizing several resources including the ODMAP protocol, and suggest that journals make protocol compliance a publication prerequisite.

The present study was conducted to understand the key ecological and biological questions of conservation importance in Drepanostachyum falcatum which aimed to map potential distribution in the western Himalayas and decipher spatial genetic structure. Eco-distribution maps were generated through ecological niche modelling using the Maximum Entropy (MaxEnt) algorithm implemented with 228 geocoordinates of species presence and 12 bioclimatic variables. Concomitantly, 26 natural populations in the western Himalayas were genetically analysed using ten genomic sequence-tagged microsatellite (STMS) markers. Model-derived distribution was adequately supported with appropriate statistical measures, such as area under the \'receiver operating characteristics (ROC)\' curve (AUC; 0.917 ± 0.034)\", Kappa (K; 0.418), normalized mutual information (NMI; 0.673) and true skill statistic (TSS; 0.715). Further, Jackknife test and response curves showed that the precipitation (pre- and post-monsoon) and temperature (average throughout the year and pre-monsoon) maximize the probabilistic distribution of D. falcatum. We recorded a wide and abundant (4096.86 km2) distribution of D. falcatum in the western Himalayas with maximum occurrence at 1500 to 2500 m asl. Furthermore, marker analysis exemplified high gene diversity with low genetic differentiation in D. falcatum. Relatively, the populations of Uttarakhand are more genetically diverse than Himachal Pradesh, whereas within the Uttarakhand, the Garhwal region captured a higher allelic diversity than Kumaon. Clustering and structure analysis indicated two major gene pools, where genetic admixing appeared to be controlled by long-distance gene flow, horizontal geographical distance, aspect, and precipitation. Both the species distribution map and population genetic structure derived herein may serve as valuable resources for conservation and management of Himalayan hill bamboos.

Correlative ecological niche modelling (ENM) is a method widely used to study the geographic distribution of species. In recent decades, it has become a leading approach for evaluating the most likely impacts of changing climate. When used to predict future distributions, ENM applications involve transferring models calibrated with modern environmental data to future conditions, usually derived from Global Climate Models (GCMs). The number of algorithms and software packages available to estimate distributions is quite high. To experimentally assess the effectiveness of correlative ENM temporal projection, we evaluated the transferability of models produced using 12 different algorithms on historical and modern data. In particular, we compared predictions generated using historical data and projected to the modern climate (simulating a \"future\" condition) with predictions generated using modern distribution and climate data. The models produced with the 12 ENM algorithms were evaluated in geographic (range size and coherence of predictions) and environmental space (Schoener\'s D index). None of the algorithms shows an overall superior capability to correctly predict future distributions. On the contrary, a few algorithms revealed an inadequate predictive ability. Finally, we provide hints that can be used as guideline to plan further studies based on the adopted general workflow, useful for all studies involving future projections.

Understanding genetic diversity and structure in natural populations and their suitable habitat response to environmental changes is critical for the protection and utilization of germplasm resources. We evaluated the genetic diversity and structure of 24 A. chinensis populations using simple sequence repeat (SSR) molecular markers. The potential suitable distribution of tetraploid A. chinensis estimated under the current climate and predicted for the future climate was generated with ecological niche modeling (ENM). The results indicated that the polyploid populations of A.chinensis have high levels of genetic diversity and that there are distinct eastern and western genetic clusters. The population structure of A. chinensis can be explained by an isolation-by-distance model. The results also revealed that potentially suitable areas of tetraploids will likely be gradually lost and the habitat will likely be increasingly fragmented in the future. This study provides an extensive overview of tetraploid A. chinensis across its distribution range, contributing to a better understanding of its germplasm resources. These results can also provide the scientific basis for the protection and sustainable utilization of kiwifruit wild resources.

BACKGROUND: The occurrence of chromosomal diseases is a worldwide health problem. The use of agrochemicals, urbanization processes, and solar radiation can be predictive factors of the elevated risk of congenital malformations. In this sense, predicting the geographical potential of the distribution of chromosomal diseases has high relevance for public health.
OBJECTIVE: This study aimed to describe chromosomal prevalence in Brazil regions, from 2005 to 2015, to model a potential distribution of chromosomal disease occurrence probability associated with land use.
METHODS: We used chromosomal prevalence to model a potential distribution of chromosomal diseases using machine learning algorithms. As the predictors of the models, we used the variables global forest canopy height, distance from the built-up area, and solar radiation. We characterized the predictive areas as potential occurrence of chromosomal diseases by land use and occupation.
RESULTS: Georeferenced data of 43,672 karyotypes detected 7,237 cases of chromosomal diseases and used 5,362 to build the models. The models generated were accurate (TSS>0.5).
CONCLUSIONS: The areas with greater occurrence of chromosomal diseases present a significant association with pasture areas, crops and agroforestry systems, and urbanized areas. This research is the first Brazilian study with this approach that seems promising in predicting the potential distribution of chromosomal diseases. Therefore, it can be an excellent management tool in public health.

The COVID-19 pandemic has accelerated the study of the potential of multi-target drugs (MTDs). The mixture of homologues called ivermectin (avermectin-B1a + avermectin-B1b) has been shown to be a MTD with potential antiviral activity against SARS-CoV-2 in vitro. However, there are few reports on the effect of each homologue on the flexibility and stiffness of proteins associated with COVID-19, described as ivermectin targets. We observed that each homologue was stably bound to the proteins studied and was able to induce detectable changes with Elastic Network Models (ENM). The perturbations induced by each homologue were characteristic of each compound and, in turn, were represented by a disruption of native intramolecular networks (interactions between residues). The homologues were able to slightly modify the conformation and stability of the connection points between the Cα atoms of the residues that make up the structural network of proteins (nodes), compared to free proteins. Each homologue was able to modified differently the distribution of quasi-rigid regions of the proteins, which could theoretically alter their biological activities. These results could provide a biophysical-computational view of the potential MTD mechanism that has been reported for ivermectin.

BACKGROUND: In order to understand how species evolutionarily responded to Plio-Pleistocene climate oscillations (e.g. in terms of speciation, extinction, migration and adaptation), it is first important to have a good understanding of those past climate changes per se. This, however, is currently limited due to the lack of global-scale climatic datasets with high temporal resolution spanning the Plio-Pleistocene. To fill this gap, I here present Oscillayers, a global-scale and region-specific bioclim dataset, facilitating the study of climatic oscillations during the last 5.4 million years at high spatial (2.5 arc-minutes) and temporal (10 kyr time periods) resolution. This dataset builds upon interpolated anomalies (Δ layers) between bioclim layers of the present and the Last Glacial Maximum (LGM) that are scaled relative to the Plio-Pleistocene global mean temperature curve, derived from benthic stable oxygen isotope ratios, to generate bioclim variables for 539 time periods. Evaluation of the scaled, interpolated estimates of palaeo-climates generated for the Holocene, Last Interglacial and Pliocene showed good agreement with independent general circulation models (GCMs) for respective time periods in terms of pattern correlation and absolute differences. Oscillayers thus provides a new tool for studying spatial-temporal patterns of evolutionary and ecological processes at high temporal and spatial resolution.
UNASSIGNED: Nineteen bioclim variables for time periods throughout the Plio-Pleistocene. Input data and R script to recreate all 19 bioclim variables.
UNASSIGNED: Global at 2.5 arc-minutes (4.65 x 4.65 = 21.62 km2 at the equator).
UNASSIGNED: The last 5.4 million years. The grain is 10 kyr (= 539 time periods).
UNASSIGNED: Data are for terrestrial climates (excluding Antarctica) taking sea level changes into account.
UNASSIGNED: All data are available as ASCII grid files.

According to current molecular evidence, the Chionaspis pinifoliae heterophyllae species complex has been recognized as 10 cryptic species. In this study, we construct potential distribution maps for seven cryptic species based on climatic variables. This was done to assess the main environmental factors that have contributed to the distribution map and test the degree of niche overlap across the seven cryptic species. We used MaxEnt to build the climatic niche models under climatic variables. For these models, the similarities and differences of the niches across the cryptic species were estimated. By comparing the potential distribution model of each cryptic species, our results suggested parapatric, sympatric and allopatry populations for this cryptic species complex. Our results showed high variability in niche overlap, and more often niche conservatism than niche divergence. The current species delimitation of the Chionaspis pinifoliae heterophyllae complex by molecular information and the hypothesis that the niche overlap in the sympatric population is higher than that of the allopatry population were supported based on the findings. This study will provide baseline data and a distribution range to facilitate the further control of these insects and formulate quarantine measures.

Cumulative environmental impact assessment (CEIA) at river basin level for hydroelectric projects is an evolving concept and has proved to be a useful tool to assess the cumulative impact of developmental projects on the natural ecosystems. However, the generality of CEIA studies is often contested because of methodological limitations, especially in the domain of biodiversity conservation and conservation planning. Ecological niche modeling (ENM) can be a useful tool in CEIA studies for conservation planning of threatened plants in hydroelectric project (HEP) areas. We elucidate this hypothesis taking the example of Lagerstroemia minuticarpa Debberm. ex P.C. Kanjilal, a critically endangered tree species in the Indian Eastern Himalaya. Standard ecological methods were employed to document occurrence records, estimate population size, and characterize habitats. ENM was used to estimate the species potential environmental niche and distribution areas. The possible impacts of HEPs on the potential habitats were predicted by overlaying the HEPs on the potential area map as well as using the conceptual network diagram. The study revealed that the species occupies an environmental niche characterized by humid to per-humid conditions, and is distributed mostly in the Lohit and Teesta basins. Potential areas of the species with high environmental suitability coincide with 19 HEPs, which point to a potential threat to the survival of the species. Network diagram indicated that project activities might deteriorate the habitats thereby affecting the population and regeneration of the species. Our study provides a framework for developing appropriate measures for species conservation and reintroduction at basin level using ENM.

Usutu virus (USUV) is a mosquito-borne flavivirus, reported in many countries of Africa and Europe, with an increasing spatial distribution and host range. Recent outbreaks leading to regional declines of European common blackbird (Turdus merula) populations and a rising number of human cases emphasize the need for increased awareness and spatial risk assessment.
Modelling approaches in ecology and epidemiology differ substantially in their algorithms, potentially resulting in diverging model outputs. Therefore, we implemented a parallel approach incorporating two commonly applied modelling techniques: (1) Maxent, a correlation-based environmental niche model and (2) a mechanistic epidemiological susceptible-exposed-infected-removed (SEIR) model. Across Europe, surveillance data of USUV-positive birds from 2003 to 2016 was acquired to train the environmental niche model and to serve as test cases for the SEIR model. The SEIR model is mainly driven by daily mean temperature and calculates the basic reproduction number R0. The environmental niche model was run with long-term bio-climatic variables derived from the same source in order to estimate climatic suitability.
Large areas across Europe are currently suitable for USUV transmission. Both models show patterns of high risk for USUV in parts of France, in the Pannonian Basin as well as northern Italy. The environmental niche model depicts the current situation better, but with USUV still being in an invasive stage there is a chance for under-estimation of risk. Areas where transmission occurred are mostly predicted correctly by the SEIR model, but it mostly fails to resolve the temporal dynamics of USUV events. High R0 values predicted by the SEIR model in areas without evidence for real-life transmission suggest that it may tend towards over-estimation of risk.
The results from our parallel-model approach highlight that relying on a single model for assessing vector-borne disease risk may lead to incomplete conclusions. Utilizing different modelling approaches is thus crucial for risk-assessment of under-studied emerging pathogens like USUV.