Mapping current and potential future distributions of the oak tree (Quercus aegilops) in the Kurdistan Region, Iraq DOI Creative Commons
Nabaz R. Khwarahm

Ecological Processes, Journal Year: 2020, Volume and Issue: 9(1)

Published: Oct. 21, 2020

Abstract Background The oak tree ( Quercus aegilops ) comprises ~ 70% of the forests in Kurdistan Region Iraq (KRI). Besides its ecological importance as residence for various endemic and migratory species, Q. forest also has socio-economic values—for example, fodder livestock, building material, medicine, charcoal, firewood. In KRI, been degrading due to anthropogenic threats (e.g., shifting cultivation, land use/land cover changes, civil war, inadequate management policy) these could increase climate changes. KRI a whole, information on current potential future geographical distributions is minimal or not existent. objectives this study were (i) predict habitat suitability species relation environmental variables change scenarios (Representative Concentration Pathway (RCP) 2.6 2070 RCP8.5 2070); (ii) determine most important controlling distribution KRI. achieved by using MaxEnt (maximum entropy) algorithm, available records , variables. Results model demonstrated that, under RCP2.6 scenarios, ranges would be reduced 3.6% (1849.7 km 2 3.16% (1627.1 ), respectively. By contrast, expand 1.5% (777.0 1.7% (848.0 was mainly controlled annual precipitation. Under centroid shift toward higher altitudes. Conclusions results suggest significant suitable range will lost preference cooler areas (high altitude) with high Conservation actions should focus mountainous establishment national parks protected areas) These findings provide useful benchmarking guidance investigation ecology forest, categorical maps can effectively used improve biodiversity conservation plans whole.

Language: Английский

Modelling current and future potential distributions of two desert jerboas under climate change in Iran DOI
Saeed Mohammadi, Elham Ebrahimi, Mohsen Shahriari Moghadam

et al.

Ecological Informatics, Journal Year: 2019, Volume and Issue: 52, P. 7 - 13

Published: April 17, 2019

Language: Английский

Citations

99
Species-Distribution Modeling: Advantages and Limitations of Its Application. 2. MaxEnt DOI
A.A. Lissovsky, Sergey V. Dudov

Biology Bulletin Reviews, Journal Year: 2021, Volume and Issue: 11(3), P. 265 - 275

Published: May 1, 2021

Language: Английский

Citations

79
Predictive mapping of two endemic oak tree species under climate change scenarios in a semiarid region: Range overlap and implications for conservation DOI Open Access

Ala A. Hama,

Nabaz R. Khwarahm

Ecological Informatics, Journal Year: 2022, Volume and Issue: 73, P. 101930 - 101930

Published: Nov. 25, 2022

Language: Английский

Citations

63
Trends in species distribution modelling in context of rare and endemic plants: a systematic review DOI Creative Commons
Ammad Waheed Qazi, Zafeer Saqib, Muhammad Zaman-ul-Haq

et al.

Ecological Processes, Journal Year: 2022, Volume and Issue: 11(1)

Published: June 8, 2022

Abstract Background Many research papers have utilized Species Distribution Models to estimate a species’ current and future geographic distribution environmental niche. This study aims (a) understand critical features of SDMs used model endemic rare species (b) identify possible constraints with the collected data. The present systematic review examined how are on plant optimal practices for research. Results evaluated literature (79 articles) was published between January 2010 December 2020. number grew considerably over time. studies were primarily conducted in Asia (41%), Europe (24%), Africa (2%). bulk (38%) focused theoretical ecology, climate change impacts (19%), conservation policy planning (22%). Most publications devoted biodiversity conservation, ecological or multidisciplinary fields. degree uncertainty not disclosed most (81%). Conclusion provides broad overview emerging trends gaps majority failed uncertainties error estimates. However, when performance estimates given, results will be highly effective, allowing more assurance predictions they make. Furthermore, based our review, we recommend that should represent levels errors modelling process.

Language: Английский

Citations

47
Distribution of important medicinal plant species in Nepal under past, present, and future climatic conditions DOI Creative Commons
Ripu M. Kunwar, Khum Bahadur Thapa‐Magar, Suresh C. Subedi

et al.

Ecological Indicators, Journal Year: 2023, Volume and Issue: 146, P. 109879 - 109879

Published: Jan. 12, 2023

Climate change is causing shifts in the habitat, distribution, ecology, and phenology of Himalayan plants. These changes are predicted to continue, jeopardizing survival medicinal plant species local livelihoods that rely on them. We analyzed present future diversity distribution influenced by different climate scenarios, calculated climatic niche using ensemble modeling (eSDM). compiled 1041 (N) geospatial data seven high-value Nepal: Aconitum spicatum (n = 100), Allium wallichii 151), Bergenia ciliata 48), Nardostachys jatamansi 121), Neopicrorhiza scrophulariiflora 94), Paris polyphylla 310) Valeriana 217) including over 85 % from field surveys rest literature online database. used bioclimatic variables Models for Interdisciplinary Research (MIROC) version MIROC6, selected Shared Socioeconomic Pathways (SSP)2-4.5 SSP5-8.5 year 2050 2070 modeling. found elevation, mean diurnal annual temperature ranges (BIO2 BIO7), precipitation warmest coldest quarters (BIO18 BIO19) be most high weight cofactors projecting potential plants Nepal. Results showed suitable range would increase concentrate mountainous areas central Nepal, but decline (sub)tropical temperate areas, suggesting both in-situ ex-situ conservation practices, respectively.

Language: Английский

Citations

31
Ecological Niche Models using MaxEnt in Google Earth Engine: Evaluation, guidelines and recommendations DOI
João C. Campos, Nuno Garcia, João Alírio

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 76, P. 102147 - 102147

Published: May 29, 2023

Language: Английский

Citations

26
Application of species distribution models to estimate and manage the Asiatic black bear (Ursus thibetanus) habitat in the Hindu Kush Mountains, Pakistan DOI
Muhammad Rehan,

Ammar Hassan,

Shah Zeb

et al.

European Journal of Wildlife Research, Journal Year: 2024, Volume and Issue: 70(3)

Published: May 31, 2024

Language: Английский

Citations

13
Predicting the potential distribution of 12 threatened medicinal plants on the Qinghai‐Tibet Plateau, with a maximum entropy model DOI
Lucun Yang, Xiaofeng Zhu, Wenzhu Song

et al.

Ecology and Evolution, Journal Year: 2024, Volume and Issue: 14(2)

Published: Feb. 1, 2024

Climate change is a vital driver of biodiversity patterns and species distributions, understanding how organisms respond to climate will shed light on the conservation endangered species. In this study, MaxEnt model was used predict potential suitable area 12 threatened medicinal plants in QTP (Qinghai-Tibet Plateau) under current future (2050s, 2070s) three scenarios (RCP2.6, RCP4.5, RCP8.5). The results showed that climatically habitats for were primarily found eastern, southeast, southern, some parts central regions QTP. Moreover, 25% would have reduced habitat areas within next 30-50 years different global warming scenarios. Among these plants, RT (

Language: Английский

Citations

11
Conservation methods for Trollius mountain flowers in Xinjiang, China under climate change: Habitat networks construction based on habitat suitability and protected areas optimization response DOI
Wenhao Fan,

Luo Yan-yun

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 376, P. 124519 - 124519

Published: Feb. 17, 2025

Language: Английский

Citations

1
Machine learning of large‐scale spatial distributions of wild turkeys with high‐dimensional environmental data DOI Creative Commons

A. Vallely Farrell,

Guiming Wang, Scott A. Rush

et al.

Ecology and Evolution, Journal Year: 2019, Volume and Issue: 9(10), P. 5938 - 5949

Published: April 24, 2019

Species distribution modeling often involves high-dimensional environmental data. Large amounts of data and multicollinearity among covariates impose challenges to statistical models in variable selection for reliable inferences the effects factors on spatial species. Few studies have evaluated compared performance multiple machine learning (ML) handling multicollinearity. Here, we assessed effectiveness removal correlated regularization cope with ML habitat suitability. Three algorithms maximum entropy (MaxEnt), random forests (RFs), support vector machines (SVMs) were applied original (OD) 27 landscape variables, reduced (RD) 14 highly being removed, 15 principal components (PC) OD accounting 90% variability. The three was measured area under curve continuous Boyce index. We collected 663 nonduplicated presence locations Eastern wild turkeys (Meleagris gallopavo silvestris) across state Mississippi, United States. Of total locations, 453 separated by a distance ≥2 km used train OD, RD, PC data, respectively. remaining 210 validate trained measure performance. had excellent RD MaxEnt SVMs good indicating adequacy default setting Weak RFs through bagging appeared alleviate resulted Regularization may help exploratory suitability wildlife.

Language: Английский

Citations

66