Risk of spread of the Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) in Ghana

The impact of invasive species on biodiversity, food security and economy is increasingly noticeable in various regions of the globe as a consequence of climate change. Yet, there is limited research on how climate change affects the distribution of the invasive Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera:Liviidae) in Ghana. Using maxnet package to fit the Maxent model in R software, we answered the following questions; (i) what are the main drivers for D. citri distribution, (ii) what are the D. citri-specific habitat requirements and (iii) how well do the risk maps fit with what we know to be correctly based on the available evidence?. We found that temperature seasonality (Bio04), mean temperature of warmest quarter (Bio10), precipitation of driest quarter (Bio17), moderate resolution imaging spectroradiometer land cover and precipitation seasonality (Bio15), were the most important drivers of D. citri distribution. The results follow the known distribution records of the pest with potential expansion of habitat suitability in the future. Because many invasive species, including D. citri, can adapt to the changing climates, our findings can serve as a guide for surveillance, tracking and prevention of D. citri spread in Ghana.

Habitat Suitability of Eastern Sarus Crane (Antigone Antigone sharpii) in Ayeyarwady Delta, the Union of Myanmar

The eastern sarus crane (Antigone antigone sharpii; ESC) is a species related to wetland ecosystems in Southeast Asia. The habitat suitability of the eastern sarus crane in Ayeyarwady Delta was surveyed between March 2018 and February 2019. Eastern sarus cranes were found at 73 locations and Maximum Entropy (MaxEnt) was used to classify the habitat suitability among different seasons. MaxEnt showed the largest total area of highly suitable habitat was in the winter season (2450 km2, AUC = 0.968), while the least amount of available suitable habitat was evident during the rainy season (1028.7 km2, AUC = 0.979). A difference in the assessment of home range areas using the Minimum Convex Polygon (95% MPC) and the Kernel Density Estimate (95% KDE) was found. The total area in the winter season was highest at 95% KDE (13,839.5 km2) and lowest in the rainy season (1238.1 km2), while 95% MCP was highest in the rainy season (7892.9 km2) and lowest in the summer season (7014.6 km2). Analysis of the environmental parameters indicated that low temperature in the summer season and high precipitation in the rainy season and winter season are important for ESC habitat suitability. These climatic parameters were important for ESC in all seasons (AUC > 0.9). Important parameters influencing ESC habitat suitability were elevation, slope, distance to road in the summer season, elevation, distance to road and village and slope in the rainy season, and elevation and slope in the winter season. Annual precipitation was the main parameter influencing ESC habitat suitability in both summer and winter, while in the rainy season it was mean diurnal range (>90%).

Predicting suitable habitats of the major forest trees in the Saïda region (Algeria): A reliable reforestation tool

Modeling potential habitat for plant species is an appropriate approach to maintain biodiversity, developing proper reforestation campaigns, and rehabilitating ecosystems. In this study, we investigated the potential distributions of four forest species, namely, Quercus faginea Lam.; Q. ilex L.; Tetraclinis articulata (Vahl) Mast.; and Pistacia atlantica Desf. In the north-western Algeria at Saïda region. The MAXENT method was used to model the habitats of these species using topographic data as predictive variables at a resolution of 100 m. Moreover, the model evaluation process was achieved using the area under the operating characteristic curve of the receiver (AUC) and Jackknife test.

The generated models were found to be accurate. AUC results are ranging between 0.98 and 0.91 for the training set and 0.87 and 0.97 for the testing set. The results of the distribution probability of this study provide a useful tool for the local decision-makers of reforestation campaigns.

Combining multiscale niche modeling, landscape connectivity, and gap analysis to prioritize habitats for conservation of striped hyaena (Hyaena hyaena)

Identifying spatial gaps in conservation networks requires information on species-environment relationships, and prioritization of habitats and corridors. We combined multi-extent niche modeling, landscape connectivity, and gap analysis to investigate scale-dependent environmental relationships, and identify core habitats and corridors for a little-known carnivore in Iran, the striped hyaena (Hyaena hyaena). This species is threatened in Iran by road vehicle collisions and direct killing. Therefore, understanding the factors that affect its habitat suitability, spatial pattern of distribution, and connectivity among them are prerequisite steps to delineate strategies aiming at human-striped hyaena co-existence. The results showed that the highest predictive power and extent of habitats was obtained at the extent sizes of 4 and 2 km, respectively. Also, connectivity analysis revealed that the extent and number of core habitats and corridors changed with increasing dispersal distance, and approximately 21% of the landscape was found to support corridors. The results of gap analysis showed that 15–17% of the core habitats overlapped with conservation areas. Given the body size of the species, its mobility, and lack of significant habitat specialization we conclude that this species would be more strongly influenced by changes in habitat amount rather than landscape configuration. Our approach showed that the scale of variables and dispersal ability must be accounted for in conservation efforts to prioritize habitats and corridors, and designing conservation areas. Our results could facilitate the conservation of striped hyaena through the identification and prioritization of habitats, establishment of conservation areas, and mitigating conflicts in corridors.

Habitat suitability model with maximum entropy approach for European roe deer (Capreolus capreolus) in the Black Sea Region

Evaluating the relationships between wildlife species and their habitats helps to predict effects of habitat change for present and future management of wild animal populations. Building ecological models are good ways to understand and manage wildlife populations and to predict various environmental scenarios. Recently, management of ungulates is becoming more important in Europe due to a high demand of hunting and their role in biodiversity. European roe deer (Capreolus capreolus) is the smallest species of cervids and has a widespread distribution in Turkey. In this study, two habitat suitability models of roe deers, living in the Black Sea Region in Turkey, were created by using a maximum entropy (MaxEnt) approach. Two wildlife development areas, which have widely different habitat types, were selected as study sites. As a result of this study, area under the curve (AUC) values were found to be above 0.80. According to the modeling results, in two different habitat types, ecological variables are quite similar in general. This study is the first study on modeling European roe deers in Turkey.

Interactive spatial scale effects on species distribution modeling: The case of the giant panda

Research has shown that varying spatial scale through the selection of the total extent of investigation and the grain size of environmental predictor variables has effects on species distribution model (SDM) results and accuracy, but there has been minimal investigation into the interactive effects of extent and grain. To do this, we used a consistently sampled range-wide dataset of giant panda occurrence across southwest China and modeled their habitat and distribution at 4 extents and 7 grain sizes. We found that increasing grain size reduced model accuracy at the smallest extent, but that increasing extent negated this effect. Increasing extent also generally increased model accuracy, but the models built at the second-largest (mountain range) extent were more accurate than those built at the largest, geographic range-wide extent. When predicting habitat suitability in the smallest nested extents (50 km2), we found that the models built at the next-largest extent (500 km2) were more accurate than the smallest-extent models but that further increases in extent resulted in large decreases in accuracy. Overall, this study highlights the impacts of the selection of spatial scale when evaluating species' habitat and distributions, and we suggest more explicit investigations of scale effects in future modeling efforts.

Species distribution model transferability and model grain size - finer may not always be better

Species distribution models have been used to predict the distribution of invasive species for conservation planning. Understanding spatial transferability of niche predictions is critical to promote species-habitat conservation and forecasting areas vulnerable to invasion. Grain size of predictor variables is an important factor affecting the accuracy and transferability of species distribution models. Choice of grain size is often dependent on the type of predictor variables used and the selection of predictors sometimes rely on data availability. This study employed the MAXENT species distribution model to investigate the effect of the grain size on model transferability for an invasive plant species. We modelled the distribution of Rhododendron ponticum in Wales, U.K. and tested model performance and transferability by varying grain size (50 m, 300 m, and 1 km). MAXENT-based models are sensitive to grain size and selection of variables. We found that over-reliance on the commonly used bioclimatic variables may lead to less accurate models as it often compromises the finer grain size of biophysical variables which may be more important determinants of species distribution at small spatial scales. Model accuracy is likely to increase with decreasing grain size. However, successful model transferability may require optimization of model grain size.

Persian leopard and wild sheep distribution modeling using the Maxent model in the Tang-e-Sayad protected area, Iran

The maximum entropy (Maxent) model was used to predict the distribution of Persian leopards and wild sheep in the Tang-e-Sayad protected area in Iran. For this purpose, eight variables, as well as 30 occurrence points of leopard and 98 points of wild sheep, were used. Two techniques, density-based occurrence points thinning and performance-based predictor variables selection were used to improve the results of the model. The model results were analyzed based on four threshold limit-based statistics (sensitivity, specificity, kappa and true skill statistics) and area under the curve (AUC), followed by determining the relative importance of variables based on the jackknife procedure. The results of threshold limit-based statistics revealed that the success of the model for distribution prediction of leopard and wild sheep were good and relatively good, respectively. According to the jackknife procedure, for wild sheep and for leopard, slope and distance to road, respectively, were the most important predictor variables. The results also indicated that the efficiency of the model did not improve by reducing the density of occurrence points for the wild sheep (AUC=0.784–0.773). However, the selection of predictor variables slightly improved the performance of the model (AUC=0.794–0.819). The results of the study also showed overlapping habitat for two species due to both human and ecological reasons for which we proposed some conservation actions such as excluding domestic grazing, controlling illegal poaching and restoration of old migratory corridors.