scholarly journals Predicting the potential distribution of striped hyena Hyaena hyaena in Iran

2020 ◽  
Vol 150 ◽  
Author(s):  
Amirhossein Dadashi-Jourdehi ◽  
Bahman Shams-Esfandabad ◽  
Abbas Ahmadi ◽  
Hamid Reza Rezaei ◽  
Hamid Toranj-Zar

Predictive potential distribution modelling is crucial in outlining habitat usage and establishing conservation management priorities. Association among species occurrence and environmental and spatial characteristics has been calculated with species distribution models. Herein, we used maximum entropy distribution modelling (MaxEnt) for predicting the potential distribution of striped hyena Hyaena hyaena in the entire country of Iran, using a number of occurrence records (i.e., 118) and environmental variables derived from remote sensing. The MaxEnt model showed a high rate of success according to AUC test scores (0.97). Our results are roughly congruent with previous studies suggesting that mountainous re-gions in northern and western Iran, and the plains in central and eastern Iran are a suitable habitat for H. hyaena.

2019 ◽  
Vol 29 (2) ◽  
pp. 109-114
Author(s):  
Amirhossein Dadashi-Jourdehi ◽  
Bahman Shams Esfandabad ◽  
Abbas Ahmadi ◽  
Hamid Reza Rezaei ◽  
Hamid Toranj-Zar

Predictive potential distribution modelling is crucial in outlining habitat usage and establishing conservation management priorities. Species distribution models estimate the relationship between species occurrences and environmental and spatial characteristics. Herein, we used maximum entropy distribution modelling (MaxEnt) for predicting the potential distribution of the striped hyena Hyaena hyaena in the entire country of Iran, using a number of occurrence records (i.e. 118) and environmental variables derived from remote sensing. The MaxEnt model had a high success rate according to test AUC scores (0.97). Our results are congruent with previous studies, suggesting that mountainous regions in northern and western Iran and the plain regions in central and eastern Iran are suitable habitats for H. hyaena.


Oryx ◽  
2016 ◽  
Vol 51 (2) ◽  
pp. 315-323 ◽  
Author(s):  
Paloma Quevedo ◽  
Achaz von Hardenberg ◽  
Hernán Pastore ◽  
José Álvarez ◽  
Paulo Corti

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.


2020 ◽  
Author(s):  
Cao Zhen ◽  
Zhang Xiaoyan ◽  
Xue Xuanji ◽  
Zhang Lei ◽  
Zhan Guanqun ◽  
...  

Abstract Background: To understand the potential distribution and habitat suitability of H. japonica in China. And to provide guidance for the wild cultivation and standardized planting of H. japonica. Methods: The maximum entropy model (Maxent) and geographic information system (ArcGIS) were applied to predict the potential suitable habitat of H. japonica species, and the contribution of variables were evaluated by using the jackknife test. Results: The AUC value confirmed the accuracy of the model prediction based on 101 occurrence records. The potential distributions of H. japonica were mainly concentrated in Jilin, Liaoning, Shaanxi and other provinces (adaptability index>0.6). Jackknife experiment showed that the precipitation of driest month (35.6%), precipitation of wettest quarter (13.4%), the mean annual temperature (7.8%) and the subclass of soil (7.8%) were the most important factors affecting the potential distribution of H. japonica. Conclusion: The niche parameters of the most suitable growth area (adaptability index>0.8) for H. japonica were precipitation of driest month (5 mm), precipitation of wettest quarter (400-490 mm), the mean annual temperature (-2-4 °C) and the subclass of soil (Glossy Chernozem, Gleyic Lime, Haplic Gypsisols).


2021 ◽  
Vol 13 (20) ◽  
pp. 11253
Author(s):  
Zhen Cao ◽  
Lei Zhang ◽  
Xinxin Zhang ◽  
Zengjun Guo

Hylomecon japonica is considered a natural medicinal plant with anti-inflammatory, anticancer and antibacterial activity. The assessment of climate change impact on its habitat suitability is important for the wild cultivation and standardized planting of H. japonica. In this study, the maximum entropy model (Maxent) and geographic information system (ArcGIS) were applied to predict the current and future distribution of H. japonica species, and the contributions of variables were evaluated by using the jackknife test. The area under the receiver operating characteristic curve (AUC) value confirmed the accuracy of the model prediction based on 102 occurrence records. The predicted potential distributions of H. japonica were mainly concentrated in Jilin, Liaoning, Shaanxi, Chongqing, Henan, Heilongjiang and other provinces (adaptability index > 0.6). The jackknife experiment showed that the precipitation of driest month (40.5%), mean annual temperature (12.4%), the precipitation of wettest quarter (11.6%) and the subclass of soil (9.7%) were the most important factors affecting the potential distribution of H. japonica. In the future, only under the shared socioeconomic Pathway 245 (SSP 245) scenario model in 2061–2080, the suitable habitat area for H. japonica is expected to show a significant upward trend. The area under other scenarios may not increase or decrease significantly.


2021 ◽  
Author(s):  
◽  
Josef Rehua Beautrais

<p>Senecio glastifolius (Asteraceae) is an invasive species in New Zealand, where it threatens rare and vulnerable coastal floristic communities. It has expanded its range dramatically over recent years and continues to spread. It is subject to control programs in parts of its distribution. Uncertainty over its future distribution and invasive impacts in New Zealand contribute to the difficulty of its management. To address this knowledge gap, the potential distribution of S. glastifolius in New Zealand was predicted, based on its bioclimatic niche.  Existing information on its current distribution and historic spread is incomplete, stored in disparate sources, and is often imprecise or inaccurate. In this study, available information on its distribution and spread was synthesised, processed, and augmented with new data collected in the field by the author. This data set was optimised for use in species distribution modelling.  The distribution of S. glastifolius is described in its native range of South Africa, plus invaded regions in Australia, the British Isles and New Zealand. The data set describing its distribution is of higher quality than any known previous data set, is more extensive, and more suitable for use in species distribution modelling. The historic spread of S. glastifolius in New Zealand is presented, illustrating its expansion from sites of introduction in Wellington, Gisborne, plus several subsequent sites, to its now considerable range throughout much of central New Zealand.  A predictive model of the potential distribution of S. glastifolius was created based on the three main climatic variables observed to limit its distribution: mean annual temperature range, aridity, and minimum temperature of the coldest month. MaxEnt models were trained on data from all regions for which georeferenced records of the species were available; South Africa, Australia, New Zealand and the Isles of Scilly. Predictions were evaluated using methods appropriate to the special case of range-expanding species. Models performed well during validation, suggesting good predictive ability when applied to new areas.  Analysis of the realised niche space of S. glastifolius in the two climatic dimensions most influencing its distribution: Annual Temperature Range and Aridity, indicated that it is exploiting almost totally disjunct niche spaces in New Zealand and South Africa. Of the climate space occupied in New Zealand, almost none is available to the species in its native range of South Africa.  Predictions of S. glastifolius’s potential distribution in New Zealand reveal significant areas of suitable habitat yet to be invaded. Much of this suitable habitat is contiguous with the current range and active dispersal front of S. glastifolius, suggesting that invasion is highly likely under a scenario of no management intervention. Specifically, it is suggested that control and surveillance in coastal Taranaki are required to prevent invasion of an area covering most of the northern third of the North Island.</p>


2021 ◽  
Vol 13 (23) ◽  
pp. 13229
Author(s):  
Wajid Rashid ◽  
Jianbin Shi ◽  
Inam ur Rahim ◽  
Muhammad Qasim ◽  
Muhammad Naveed Baloch ◽  
...  

The snow leopard (Panthera uncia) is a cryptic and rare big cat inhabiting Asia’s remote and harsh elevated areas. Its population has decreased across the globe for various reasons, including human–snow leopard conflicts (HSCs). Understanding the snow leopard’s distribution range and habitat interactions with human/livestock is essential for understanding the ecological context in which HSCs occur and thus gives insights into how to mitigate HSCs. In this study, a MaxEnt model predicted the snow leopard’s potential distribution and analyzed the land use/cover to determine the habitat interactions of snow leopards with human/livestock in Karakoram–Pamir, northern Pakistan. The results indicated an excellent model performance for predicting the species’ potential distribution. The variables with higher contributions to the model were the mean diurnal temperature range (51.7%), annual temperature range (18.5%), aspect (14.2%), and land cover (6.9%). The model predicted approximately 10% of the study area as a highly suitable habitat for snow leopards. Appropriate areas included those at an altitude ranging from 2721 to 4825 m, with a mean elevation of 3796.9 ± 432 m, overlapping between suitable snow leopard habitats and human presence. The human encroachment (human settlements and agriculture) in suitable snow leopard habitat increased by 115% between 2008 and 2018. Increasing encroachment and a clear overlap between snow leopard suitable habitat and human activities, signs of growing competition between wildlife and human/livestock for limited rangeland resources, may have contributed to increasing HSCs. A sound land use plan is needed to minimize overlaps between suitable snow leopard habitat and human presence to mitigate HSCs in the long run.


Author(s):  
Daniel Becker ◽  
Christian Willmes ◽  
Georg Bareth ◽  
Gerd-Christian Weniger

This contribution describes the development of a plugin for the geographic information system QGIS to interface the openModeller software package. The aim is to use openModeller to generate species’ potential distribution models for various archaeological applications (site catchment analysis, for example). Since the usage of openModeller’s command-line interface and configuration files can be a bit inconvenient, an extension of the QGIS user interface to handle these tasks, in combination with the management of the geographic data, was required. The implementation was realized in Python using PyQGIS and PyQT. The plugin, in combination with QGIS, handles the tasks of managing geographical data, data conversion, generation of configuration files required by openModeller and compilation of a project folder. The plugin proved to be very helpful with the task of compiling project datasets and configuration files for multiple instances of species occurrence datasets and the overall handling of openModeller. In addition, the plugin is easily extensible to take potential new requirements into account in the future.


2021 ◽  
Author(s):  
◽  
Josef Rehua Beautrais

<p>Senecio glastifolius (Asteraceae) is an invasive species in New Zealand, where it threatens rare and vulnerable coastal floristic communities. It has expanded its range dramatically over recent years and continues to spread. It is subject to control programs in parts of its distribution. Uncertainty over its future distribution and invasive impacts in New Zealand contribute to the difficulty of its management. To address this knowledge gap, the potential distribution of S. glastifolius in New Zealand was predicted, based on its bioclimatic niche.  Existing information on its current distribution and historic spread is incomplete, stored in disparate sources, and is often imprecise or inaccurate. In this study, available information on its distribution and spread was synthesised, processed, and augmented with new data collected in the field by the author. This data set was optimised for use in species distribution modelling.  The distribution of S. glastifolius is described in its native range of South Africa, plus invaded regions in Australia, the British Isles and New Zealand. The data set describing its distribution is of higher quality than any known previous data set, is more extensive, and more suitable for use in species distribution modelling. The historic spread of S. glastifolius in New Zealand is presented, illustrating its expansion from sites of introduction in Wellington, Gisborne, plus several subsequent sites, to its now considerable range throughout much of central New Zealand.  A predictive model of the potential distribution of S. glastifolius was created based on the three main climatic variables observed to limit its distribution: mean annual temperature range, aridity, and minimum temperature of the coldest month. MaxEnt models were trained on data from all regions for which georeferenced records of the species were available; South Africa, Australia, New Zealand and the Isles of Scilly. Predictions were evaluated using methods appropriate to the special case of range-expanding species. Models performed well during validation, suggesting good predictive ability when applied to new areas.  Analysis of the realised niche space of S. glastifolius in the two climatic dimensions most influencing its distribution: Annual Temperature Range and Aridity, indicated that it is exploiting almost totally disjunct niche spaces in New Zealand and South Africa. Of the climate space occupied in New Zealand, almost none is available to the species in its native range of South Africa.  Predictions of S. glastifolius’s potential distribution in New Zealand reveal significant areas of suitable habitat yet to be invaded. Much of this suitable habitat is contiguous with the current range and active dispersal front of S. glastifolius, suggesting that invasion is highly likely under a scenario of no management intervention. Specifically, it is suggested that control and surveillance in coastal Taranaki are required to prevent invasion of an area covering most of the northern third of the North Island.</p>


Author(s):  
Hongjun Jiang ◽  
Ting Liu ◽  
Shiping Gao ◽  
Ruijun Wang ◽  
Ruchun Zhang ◽  
...  

Aim:Artemisia annua L. is the one and only original plant used to isolate artemisinin which is a highly effective remedy to fight malaria. Climate change leads to change of distribution and suitable range for many species and A. annua is no exception. However, it is not clear that the potential distribution and suitable range change of this unique plant under climate change. Therefore, we present this research to study its change in the future. Location: Global. Methods: Since the accuracy of species distribution models was affected by occurrence records and environmental variables, 1062 presence records and 7 variables were picked out to build ensemble models with 10 different algorithms by means of biomod2 under current and future climate scenarios. Results: At present, except SRE, the AUC values of the rest models were greater than 0.8, and the TSS values were greater than 0.6, the values of ensemble model were 0.968 and 0.826 respectively. Mean temperature of driest quarter was the dominant factor to shape the range of A. annua and its optimum interval ranged from 4.8 to 23.3ºC. The high suitable habitats of A. annua were mainly located in Eastern Asia, Western Europe, Central Europe. In the future, the high suitable area would decline at 15.55% to 25.87%. Main conclusions: Ensemble models showed it performed better than any the single one. At present, the high suitable habitat simulated by ensemble model was in accordance with the actual occurrence records. In the future, the high suitable habitat for A. annua would move northeast, and disappear in North America. They would increase with time under each SSP, but sharply decline while comparing with the current one. This study can be used to protect wild resource and guide cultivation for A. annua, which would make modest contribution to fight malaria.


Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 190 ◽  
Author(s):  
Keliang Zhang ◽  
Yin Zhang ◽  
Jun Tao

A detailed understanding of species distribution is usually a prerequisite for the rehabilitation and utilization of species in an ecosystem. Paeonia veitchii (Paeoniaceae), which is an endemic species of China, is an ornamental and medicinal plant that features high economic and ecological values. With the decrease of its population in recent decades, it has become a locally endangered species. In present study, we modeled the potential distribution of P. veitchii under current and future conditions, and evaluated the importance of the factors that shape its distribution. The results revealed a highly and moderately suitable habitat for P. veitchii that encompassed ca. 605,114 km2. The central area lies in northwest Sichuan Province. Elevation, temperature seasonality, annual mean precipitation, and precipitation seasonality were identified as the most important factors shaping the distribution of P. veitchii. Under the scenario with a low concentration of greenhouse gas emissions (RCP 2.6), we predicted an overall expansion of the potential distribution by 2050, followed by a slight contraction in 2070. However, with the scenario featuring intense greenhouse gas emissions (RCP 8.5), the range of suitable habitat should increase with the increasing intensity of global warming. The information that was obtained in the present study can provide background information related to the long-term conservation of this species.


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