Potential distribution models from two highly endemic species of subterranean rodents of Argentina: which environmental variables have better performance in highly specialized species?

2021 ◽  
Author(s):  
Ailin Austrich ◽  
Marcelo J. Kittlein ◽  
Matías S. Mora ◽  
Fernando J. Mapelli
Keyword(s):  
Endemic Species ◽  
Environmental Variables ◽  
Potential Distribution ◽  
Subterranean Rodents ◽  
Distribution Models

scholarly journals Comparing six different species distribution models with several subsets of environmental variables: Predicting the potential current distribution of Guettarda speciosa in Indonesia

2019 ◽  
Vol 20 (8) ◽  
Author(s):  
Angga Yudaputra ◽  
Inggit Puji Astuti ◽  
Wendell P. Cropper
Keyword(s):  
Distribution Pattern ◽  
Predictive Model ◽  
Species Distribution ◽  
Current Distribution ◽  
Predictive Models ◽  
Environmental Variables ◽  
Potential Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Predictive Map

Abstract. Yudaputra A, Pujiastuti I, Cropper Jr. WP. 2019. Comparing six different species distribution models with several subsets of environmental variables: predicting the potential current distribution of zebra Guettarda speciosa in Indonesia. Biodiversitas 20: 2321-2328. There are many algorithms of species distribution modeling that widely used to predict the potential distribution pattern of diverse organisms. Finding the best model in terms of predicting the potential distribution of many species remains a challenge. The objective of this study is to compare six different algorithms for predicting the potential current distribution pattern of Guettarda speciosa (zebra wood). The occurrence records of G. speciosa are derived from herbarium database, Bogor Botanic Gardens’s plant inventory database and direct field surveys through NKRI expedition.  Seven climatic variables and elevation data are extracted from global data. R open-source software is used to run those algorithms and QGIS is used to prepare the spatial data.  The result shows that MAXENT outperforms other predictive models with the highest AUC score 0.89, followed by SVM (0.87), RF (0.86), and GLM (0.82), DOMAIN (0.73), and BIOCLIM (0.62). Based on the AUC score, the four predictive models (MAXENT, SVM, RF, GLM) are categorized into good predictive models, indicating those are quite better to predict the potential current distribution pattern of G. speciosa. Whereas, DOMAIN is fair predictive model and BIOCLIM is poor predictive model. The predictive map derived from four models (MAXENT, SVM, RF, and GLM) shows almost similar appearance in predicting of potential current distribution of G. speciosa. The predictive map of current distribution would be useful to provide information regarding the potential habitat of G. speciosa across the landscape of Indonesia.

scholarly journals Endemic Juniperus Montane Species Facing Extinction Risk under Climate Change in Southwest China: Integrative Approach for Conservation Assessment and Prioritization

Biology ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 63
Author(s):  
Mohammed A. Dakhil ◽  
Marwa Waseem A. Halmy ◽  
Walaa A. Hassan ◽  
Ali El-Keblawy ◽  
Kaiwen Pan ◽  
...  
Keyword(s):  
Climate Change ◽  
Potential Distribution ◽  
Extinction Risk ◽  
In Situ Conservation ◽  
Integrative Approach ◽  
Conservation Assessment ◽  
Distribution Models ◽  
Area Of Occupancy ◽  
Annual Range ◽  
The Impact

Climate change is an important driver of biodiversity loss and extinction of endemic montane species. In China, three endemic Juniperus spp. (Juniperuspingii var. pingii, J.tibetica, and J.komarovii) are threatened and subjected to the risk of extinction. This study aimed to predict the potential distribution of these three Juniperus species under climate change and dispersal scenarios, to identify critical drivers explaining their potential distributions, to assess the extinction risk by estimating the loss percentage in their area of occupancy (AOO), and to identify priority areas for their conservation in China. We used ensemble modeling to evaluate the impact of climate change and project AOO. Our results revealed that the projected AOOs followed a similar trend in the three Juniperus species, which predicted an entire loss of their suitable habitats under both climate and dispersal scenarios. Temperature annual range and isothermality were the most critical key variables explaining the potential distribution of these three Juniperus species; they contribute by 16–56.1% and 20.4–38.3%, respectively. Accounting for the use of different thresholds provides a balanced approach for species distribution models’ applications in conservation assessment when the goal is to assess potential climatic suitability in new geographical areas. Therefore, south Sichuan and north Yunnan could be considered important priority conservation areas for in situ conservation and search for unknown populations of these three Juniperus species.

Ignoring seasonal changes in the ecological niche of non-migratory species may lead to biases in potential distribution models: lessons from bats

2018 ◽  
Vol 27 (9) ◽  
pp. 2425-2441 ◽  
Author(s):  
Sonia Smeraldo ◽  
Mirko Di Febbraro ◽  
Luciano Bosso ◽  
Carles Flaquer ◽  
David Guixé ◽  
...  
Keyword(s):  
Seasonal Changes ◽  
Ecological Niche ◽  
Potential Distribution ◽  
Migratory Species ◽  
Distribution Models

Climate Defined but Not Soil-restricted: The Distribution of a Neotropical tree through Space and Time

2021 ◽  
Author(s):  
Facundo Alvarez ◽  
Paulo S Morandi ◽  
Ben Hur Marimon-Junior ◽  
Reginal Exavier ◽  
Igor Araújo ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Environmental Variables ◽  
Distribution Patterns ◽  
Distribution Models ◽  
Climatic Fluctuations ◽  
Space And Time ◽  
Predictive Variables ◽  
Soil Variables ◽  
Neotropical Tree ◽  
Edaphic Variables

Abstract AimsBrosimum rubescens, a tree species with a Neotropical distribution, can achieve local monodominance in Southern Amazonia forests. Understanding how and why this species varies across space and time is important because the monodominance of some species alters ecosystems complexity. Here we evaluate the fundamental ecological niche of B. rubescens by species distribution models (SDM), combining predictive environmental variables with occurrence points. We specifically aim to 1) determine how the spatial distribution patterns of B. rubescens vary with different environmental predictive variables, and 2) evaluate the temporal and spatial persistence of B. rubescens in the Neotropics. MethodsTo generate the SDMs, the predictive environmental variables were incorporated as main components of climatic, hydric and soil variables. ResultsAll algorithms show higher performance in spatial predictions for hydric variables and for the combination of climatic, hydric and edaphic variables. We identified that the potential niches of B. rubescens seem to be defined by climatic fluctuations, with the edaphic conditions being predictive variables that are not restrictive of their presence on the evaluated spatial scale. From the LMG (Last Glacial Maximum) to the present, the species seems to have increased its spatial amplitude; however, from the present to the future, predictions suggest that B. rubescens will experience a considerable loss of its range. ConclusionsOur findings show the independent and combined effects of different environmental variables, allowing us to identify which limit or facilitate the spatial distribution of B. rubescens. We corroborate the spatial persistence and geographical fidelity of the species' spatial patterns over time.

scholarly journals Maxent Data Mining Technique and Its Comparison with a Bivariate Statistical Model for Predicting the Potential Distribution of Astragalus Fasciculifolius Boiss. in Fars, Iran

2019 ◽  
Vol 11 (12) ◽  
pp. 3452 ◽  
Author(s):  
Marjaneh Mousazade ◽  
Gholamabbas Ghanbarian ◽  
Hamid Reza Pourghasemi ◽  
Roja Safaeian ◽  
Artemi Cerdà
Keyword(s):  
Data Mining ◽  
Statistical Model ◽  
Habitat Suitability ◽  
Environmental Variables ◽  
Potential Distribution ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Suitability Map ◽  
Habitat Suitability Map ◽  
Edaphic Variables

The identification of geographical distribution of a plant species is crucial for understanding the importance of environmental variables affecting plant habitat. In the present study, the spatial potential distribution of Astragalus fasciculifolius Boiss. as a key specie was mapped using maximum entropy (Maxent) as data mining technique and bivariate statistical model (FR: frequency ratio) in marl soils of southern Zagros, Iran. The A. fasciculifolius locations were identified and recorded by intensive field campaigns. Then, localities points were randomly split into a 70% training dataset and 30% for validation. Two climatic, four topographic, and eight edaphic variables were used to model the A. fasciculifolius distribution and its habitat potential. Maps of environmental variables were generated using Geographic Information System (GIS). Next, the habitat suitability index (HSI) maps were produced and classified by means of Maxent and FR approaches. Finally, the area under the receiver operating characteristic (AUC-ROC) curve was used to compare the performance of maps produced by Maxent and FR models. The interpretation of environmental variables revealed that the climatic and topographic parameters had less impact compared to edaphic variables in habitat distribution of A. fasciculifolius. The results showed that bulk density, nitrogen, acidity (pH), sand, and electrical conductivity (EC) of soil are the most significant variables that affect distribution of A. fasciculifolius. The validation of results showed that AUC values of Maxent and FR models are 0.83 and 0.76, respectively. The habitat suitability map by the better model (Maxent) showed that areas with high and very high suitable classes cover approximately 22% of the study area. Generally, the habitat suitability map produced using Maxent model could provide important information for conservation planning and a reclamation project of the degraded habitat of intended plant species. The distribution of the plants identifies the water, soil, and nutrient resources and affects the fauna distribution, and this is why it is relevant to research and to understand the plant distribution to properly improve the management and to achieve a sustainable management.

scholarly journals Predicting the potential distribution of the Endangered huemul deer Hippocamelus bisulcus in North Patagonia

Oryx ◽  
2016 ◽  
Vol 51 (2) ◽  
pp. 315-323 ◽  
Author(s):  
Paloma Quevedo ◽  
Achaz von Hardenberg ◽  
Hernán Pastore ◽  
José Álvarez ◽  
Paulo Corti
Keyword(s):  
Potential Distribution ◽  
Summer Precipitation ◽  
Suitable Habitat ◽  
Large Mammals ◽  
Distribution Models ◽  
North Patagonia ◽  
Predictive Variables ◽  
Hippocamelus Bisulcus ◽  
Best Fitting ◽  
Fitting Model

AbstractHabitat loss is one of the main threats to wildlife, particularly large mammals. Estimating the potential distribution of threatened species to guide surveys and conservation is crucial, primarily because such species tend to exist in small fragmented populations. The Endangered huemul deer Hippocamelus bisulcus is endemic to the southern Andes of Chile and Argentina. Although the species occurs in the Valdivian Ecoregion, a hotspot for biodiversity, we have no information on its occupancy and potential distribution in this region. We built and compared species distribution models for huemul using the maximum entropy approach, using 258 presence records and sets of bioclimatic and geographical variables as predictors, with the objective of assessing the potential distribution of the species in the Valdivian Ecoregion. Annual temperature range and summer precipitation were the predictive variables with the greatest influence in the best-fitting model. Approximately 12,360 km2 of the study area was identified as suitable habitat for the huemul, of which 30% is included in the national protected area systems of Chile and Argentina. The map of potential distribution produced by our model will facilitate prioritization of future survey efforts in other remote and unexplored areas in which huemul have not been recorded since the 1980s but where there is a high probability of their occurrence.

scholarly journals Assessing the Potential Distribution of Asian Gypsy Moth in Canada: A Comparison of Two Methodological Approaches

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Vivek Srivastava ◽  
Verena C. Griess ◽  
Melody A. Keena
Keyword(s):  
Gypsy Moth ◽  
Potential Distribution ◽  
Climate Change Scenarios ◽  
Distribution Models ◽  
Economic Threat ◽  
Asian Gypsy Moth ◽  
Rule Set ◽  
Special Concern ◽  
European Counterpart ◽  
Cellular Automation Model

AbstractGypsy moth (Lymantria dispar L.) is one of the world’s worst hardwood defoliating invasive alien species. It is currently spreading across North America, damaging forest ecosystems and posing a significant economic threat. Two subspecies L. d. asiatica and L. d. japonica, collectively referred to as Asian gypsy moth (AGM) are of special concern as they have traits that make them better invaders than their European counterpart (e.g. flight capability of females). We assessed the potential distribution of AGM in Canada using two presence-only species distribution models, Maximum Entropy (MaxEnt) and Genetic Algorithm for Rule-set Prediction (GARP). In addition, we mapped AGM potential future distribution under two climate change scenarios (A1B and A2) while implementing dispersal constraints using the cellular automation model MigClim. MaxEnt had higher AUC, pAUC and sensitivity scores (0.82/1.40/1.00) when compared to GARP (0.70/1.26/0.9), indicating better discrimination of suitable versus unsuitable areas for AGM. The models indicated that suitable conditions for AGM were present in the provinces of British Columbia, Ontario, Quebec, Nova Scotia and New Brunswick. The human influence index was the variable found to contribute the most in predicting the distribution of AGM. These model results can be used to identify areas at risk for this pest, to inform strategic and tactical pest management decisions.

scholarly journals Climate change influences on the potential distribution of Dianthus polylepis Bien. ex Boiss. (Caryophyllaceae), an endemic species in the Irano-Turanian region

PLoS ONE ◽  
2020 ◽  
Vol 15 (8) ◽  
pp. e0237527
Author(s):  
Maryam Behroozian ◽  
Hamid Ejtehadi ◽  
A. Townsend Peterson ◽  
Farshid Memariani ◽  
Mansour Mesdaghi
Keyword(s):  
Climate Change ◽  
Endemic Species ◽  
Potential Distribution
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