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MaxEnt analysis of Ochotona rufescens, Oumm Qatafa
<p>For pika, we collected the coordinates of 35 find spots of recent and sub-recent <em>O. rufescens</em> (Čermák et al. 2006; Khaki-Saneh 2014), a set of rasters representing current (1979 - 2013) climate based on standard 19 bioclim variables at 10-min resolution (Anthropocene, v1.2b: <a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). These data were used to construct a Maximum Entropy model for the current distribution of the Afghan pika using the ‘maxnet’ package (Phillips 2021) in R (version 4.0.2). Other libraries used include ‘terra’ (Hijman 2021) and ‘modEvA’ (Barbosa et al. 2013). The model provided a list of variables that parsimoniously predict suitable environments for the Afghan pika, and also a projection of the probability of finding suitable habitats, as defined by the bioclimatic variables, in geographical space under present-day conditions. The values of the selected model bioclimatic variables at the present find spots were compared with the same values for Oumm Qatafa to examine its present climatic suitability as a habitat for pikas.</p> <p>Barbosa, A.M., Real, R., Munoz, A.R. & Brown, J.A. (2013). New measures for assessing model equilibrium and prediction mismatch in species distribution models. Diversity and Distributions, 19(10), 1333-1338. https://onlinelibrary.wiley.com/doi/full/10.1111/ddi.12100.</p> <p>Čermák, S., Obuch, S., Benda, P. (2006). Notes on the genus <em>Ochotona</em> in the Middle East (Lagomorpha: Ochotonidae). <em>Lynx</em> (Praha) 37, 51–66.</p> <p>Hijmans, R.J. (2021). terra: Spatial Data Analysis. R package version 1.5-8. https://rspatial.org/terra/</p> <p>Khaki Sahneh, S., Nouri, Z., Alizadeh Shabani, A., Dehdar Dargahi, M. (2014). A review on habitats selection by Afghan Pika (<em>Ochotona rufescens</em>), case study: the Lashgardar protected area in Hamadan Province. <em>Journal on New Biological Reports</em> 3(3), 186–199.</p> <p>Phillips, S. (2021). maxnet: Fitting 'Maxent' Species Distribution Models with 'glmnet'. R package version 0.1.4. https://CRAN.R-project.org/package=maxnet.</p>
Fig. 8. Heat map resulting from the Species Distribution Model using MaxEnt, where 1 in A revision of the genus Armillipora Quate (Diptera: Psychodidae) with the descriptions of two new species
Fig. 8. Heat map resulting from the Species Distribution Model using MaxEnt, where 1 is equal to the highest probability of distribution, while 0 is the lowest probability.
Fig. 6 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria
Fig. 6. Abundance (mean number of burrows/100 × 5 m transect) of S. citellus in 4 colonies in the study area in summer (for the period 2017–2021) N = Luda Yana; –– l –– = Belotrup; ---- l ---- = Panagyurski kolonii; u = Beli Manastiri
Fig. 5 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria
Fig. 5. Changes in the habitat suitability in the study area (white – not suitable, black – high suitability) of European souslik assessed by maxent modelling based on data from 2006–2018 (B) and extrapolated for the period 1985–2005 (A). The results are presented in
Fig. 3 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria
Fig. 3. Negative and positive anomalies (white and black bars) of the Mean Annual Temperature time series for the period of 1985–2018 (data from the meteorological station Sofia)
Fig. 2 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria
Fig. 2. Changes in the number of grazing livestock in the southern central Bulgarian planning region for the period 2001–2018
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study. in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study.
Fig. 1 in Maximum entropy niche-based modeling (Maxent) of potential geographical distribution of Coreura albicosta (Lepidoptera: Erebidae: Ctenuchina) in Mexico
Fig. 1. Model of potential distribution of Coreura albicosta with enhancement of the favorable climatic regions for this species, and superposition with the network of protected areas of México. Gray: lower probability of appropriate environmental conditions for distribution of the species. Light gray sections represent the decision threshold (0.2426) in which the grids are favorable for the distribution of the species. Darker sections inside light gray: areas with high probability of presence of the species. Black dots indicate the known distribution of the species. The protected areas are represented with a black line.
Fig. 7. MAXENT reconstruction for clade I in Fig. 11. Left G1s in Tuerkayana latens, a New Species of Land Crab from French Polynesia, with a Discussion on the Phylogeny of the Genus (Crustacea: Decapoda: Brachyura: Gecarcinidae)
Fig. 7. MAXENT reconstruction for clade I (A) and clade III (B) of Amphibalanus amphitrite in the world representing current distribution models.
Fig. 3 in New record of a blood-feeding terrestrial leech, Haemadipsa rjukjuana Oka, 1910 (Haemadipsidae, Arhynchobdellida) on Heuksando Island and possible habitat estimation in the current and future Korean Peninsula using a Maxent model
Fig. 3. Current (A and F) and future distribution models (B-E, G-J) for Haemadipsa rjukjuana in Korea. Dark gray represents over 0.5 MaxEnt value (suitable habitat) and light gray represents below 0.5 (unsuitable habitat). A is projected to the current climate conditions (2020), and F was built with the restricted spatial area between Heuksando Island and Gageodo Island. B-E are projections of the Maxent model to SSP585 of GISS-E2-1 climate scenarios by NASA and G-J were SSP585 of INM-CM4-8 scenarios by The Institute of Numerical Mathematics. B-E and G-J are respectively 2040, 2060, 2080, and 2100.
Fig. 2 in New record of a blood-feeding terrestrial leech, Haemadipsa rjukjuana Oka, 1910 (Haemadipsidae, Arhynchobdellida) on Heuksando Island and possible habitat estimation in the current and future Korean Peninsula using a Maxent model
Fig. 2. Projection of MaxEnt Haemadipsa rjukjuana distribution model from Heuksando Island and Gageodo Island to the current climate condition of South Korea. Red color (lower value) represents less suitable habitats and blue (higher value close to 1.0) represents suitable habitats for H. rjukjuana.
Fig. 1 in New record of a blood-feeding terrestrial leech, Haemadipsa rjukjuana Oka, 1910 (Haemadipsidae, Arhynchobdellida) on Heuksando Island and possible habitat estimation in the current and future Korean Peninsula using a Maxent model
Fig. 1. The map of study sites (inset) and the Korean Peninsula. Haemadipsa rjukjuana was identified from the regions shaded in gray.
Figure 6. Response curve showing how each environmental variable affected the Maxent prediction. Variables A, B in PhylOgeOgraphy and pOtential distributiOn OF Sturnira lilium and S. giannae (ChirOptera: PhyllOstOmidae) With range eXtensiOn FOr S. giannae in the CerradO and Pantanal biOmes
Figure 6. Response curve showing how each environmental variable affected the Maxent prediction. Variables A, B, and C were the ones that most affected the potential distribution of S. giannae, and D, E, and F most affected the potential distribution of S. lilium. The curves show the average response of the 10 replicate Maxent runs (red) and the standard deviation (blue).
Figure 6 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 6. Total cross-validation AUC (CV-AUC) and spatial congruence AUC (SC- AUC) for a range of grain sizes.
Figure 4 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 4. Divergence between the uncorrelated and pruned models estimated through Parolo divergence index at 250-m resolution. As is shown, there was little divergence (0– 0.2) between models in most of the study area.
Figure 1 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 1. The location of the study area on a map of western Asia (right). Inset shows DEM of study area with polygons indicating the protected areas where populations of goitered gazelle occur.
Figure 5 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 5. The change in performance index (AUC, left) and habitat suitability area (right) of the output model with increasing extent size (open circles) and grain size (filled circles) from 250 to 3000 m.
Figure 3 in Maxent modeling for predicting potential distribution of goitered gazelle in central Iran the effect of extent and grain size on performance of the model
Figure 3. Goitered gazelle distribution maps based on the uncorrelated model (left) and the pruned model (right) for the 250-m grid size.
Prediction of the potentially suitable areas of Leonurus japonicus with the optimized MaxEnt model
<p><em><span>Leonurus japonicus </span></em><span>Houtt.</span><span> is a traditional Chinese medicinal plant with high medicinal and edible value.</span><span> Wild <em>L. japonicus</em> resources have been reduced dramatically in recent years. This study predicted the response of distribution range of <em>L. japonicus</em> to climate change in China, which provided the scientific basis for the conservation and utilization. In this study, 489 occurrence poin</span><span>ts</span><span> of <em>L. japonicus</em> were selected based on GIS technology and spThin package. The default parameters of the Maxent model were adjusted by using ENMeva1 package of the R environment, and the optimized Maxent model was used to analyze the distribution of <em>L. japonicus</em>. When the feature combination in the model parameters is hing and the regularization multiplier is 1.5, the Maxent model has a higher degree of optimization. With the AUC of 0.830 our model showed a good predictive performance The results showed that <em>L. japonicus</em> was widely distributed in the current period. The maximum temperature of the warmest month, the minimum temperature of the coldest </span><span>month</span><span>, the precipitation of the wettest month, the precipitation of the driest month and altitude were the main environmental factors affecting the distribution of <em>L. japonicus</em>. Under the three climate change scenarios, the suitable distribution area of <em>L. japonicus</em> will range-shift to high latitudes, indicating that the distribution of <em>L. japonicus</em> has a strong response to climate change. The regional change rate is the lowest under the SSP126-2090s scenario and the highest under the SSP585-2090s scenario.</span></p>
Prediction of the potentially suitable areas of Leonurus japonicus with the optimized MaxEnt model
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