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species distribution modelling performance and its implication for Sentinel-2-based prediction of invasive Prosopis juliflora in lower Awash River basin, Ethiopia

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Ecological Processes 2021年第1期10卷 250-265页

作者： Nurhussen Ahmed Clement Atzberger Worku Zewdie Department of Remote Sensing Entoto Observatory and Research CenterEthiopian Space Science and Technology Institute(ESSTI)Addis AbabaEthiopia. Department of Geography and Environmental Studies Wollo UniversityDessieEthiopia. Institute of Geomatics University of Natural Resources and Life SciencesVienna(BOKU)Peter JordanStraße 821190 ViennaAustria.

Background:species distribution modelling(SDM)coupled with freely available multispectral imagery from Sentinel-2(S2)satellite provides an immense contribution in monitoring invasive ***,attempts to evaluate the performances of SDMs using S2 spectral bands and S2 Radiometric Indices(S2-RIs)and biophysical variables,in particular,were ***,this study aimed at evaluating the performance of six commonly used SDMs and one ensemble model for S2-based variables in modelling the current distribution of Prosopis juliflora in the lower Awash River basin,***-five variables were computed from Sentinel-2B level-2A,and out of the variables,twelve significant variables were selected using Variable Inflation Factor(VIF).A total of 680 presence and absence data were collected to train and validate variables using the tenfold bootstrap replication approach in the R software“sdm”*** performance of the models was evaluated using sensitivity,specificity,True Skill Statistics(TSS),kappa coefficient,area under the curve(AUC),and ***:Our findings demonstrated that except bioclim all machine learning and regression models provided successful *** the tested models,Random Forest(RF)performed better with 93%TSS and 99%AUC followed by Boosted Regression Trees(BRT),ensemble,Generalized Additive Model(GAM),Support Vector Machine(SVM),and Generalized Linear Model(GLM)in decreasing *** relative influence of vegetation indices was the highest followed by soil indices,biophysical variables,and water indices in decreasing *** to RF prediction,16.14%(1553.5 km^(2))of the study area was invaded by the alien ***:Our results highlighted that S2-RIs and biophysical variables combined with machine learning and regression models have a higher capacity to model invasive species ***,the use of machine learning algorithms such as RF algorithm is highly essential for remote sensing-based inva

关键词： Biophysical processors Prosopis juliflora Radiometric indices Remote sensing Sentinel-2 species distribution modelling

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Trends in species distribution modelling in context of rare and endemic plants:a systematic review

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Ecological Processes 2022年第1期11卷 525-535页

作者： Ammad Waheed Qazi Zafeer Saqib Muhammad Zaman‑ul‑Haq Department of Environmental Science International Islamic University IslamabadIslamabad 46000Pakista

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

关键词： species distribution modelling Endemic Rare Uncertainty measures Model validation

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modelling the spatial distribution of snake species in northwestern Tunisia using maximum entropy(Maxent) and Geographic Information System(GIS)

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Journal of Forestry Research 2018年第1期29卷 233-245页

作者： Mohsen Kalboussi Hammadi Achour Institut Sylvo-pastoral de Tabarka Universit6 de Jendouba8110 Tabarka Tunisia Unite de recherche 《Geomatique des Geosystemes》 02/UR/10-01 Universite de la Mannouba Campus universitaire B.P. 95 2010 Mannouba Tunisia

We used GIS and maximum entropy to predict the potential distribution of six snake species belong to three families in Kroumiria（Northwestern Tunisia）： Natricidae（Natrix maura and Natrix astreptophora）, Colubridae（Hemorrhois hippocrepis, Coronella girondica and Macroprotodon mauritanicus）, and Lamprophiidae（Malpolon insignitus）. The suitable habitat for each species was modelled using the maximum entropy algorithm, combining presence field data（collected during 16 years：2000–2015） with a set of seven environmental variables（mean annual precipitation, elevation, slope gradient,aspect, distance to watercourses, land surface temperature and normalized Differential Vegetation Index. The relative importance of these environmental variables was evaluated by jackknife tests and the predictive power of our models was assessed using the area under the receiver operating characteristic. The main explicative variables of the species distribution were distance from streams and elevation, with contributions ranging from 60 to 77 and from 10 to 25%,respectively. Our study provided the first habitat suitability models for snakes in Kroumiria and this information can be used by conservation biologists and land managers concerned with preserving snakes in Kroumiria.

关键词： species distribution modelling Maxent Snakes Kroumiria Tunisia

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Conservation of challenging endemic plant species at high risk of extinction in arid mountain ecosystems:a case study of Rosa arabica Cré*** Egypt

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Journal of Mountain Science 2021年第10期18卷 2698-2721页

作者： Karim OMAR Ibrahim ELGAMAL Nature Conservation Sector Ministry of EnvironmentCairo 11728Egypt United Nation Development Program(UNDP) Cairo 2007Egypt

Rosa arabica Cré*** a perennial shrub belonging to the family *** is endemic to the high mountain area of *** Protected Area(SCPA)in southern Sinai,Egypt,and is listed as one of the most 100 threatened plants in the ***,it has been listed as critically endangered by IUCN Red List due to its small extent of occurrence and tiny population *** reported the continuous decline in habitat quality for this species and the urgent need to carry out on-ground conservation ***,this research aims to conserve Rosa arabica through in situ practices by implementing the following steps,respectively:a)evaluate the current conservation status through IUCN Red List to extract the environmental factors controlling the species’distribution necessary for establishing the recovery program,b)determine the potential species habitat suitability under the current climate conditions using Maxent,and c)based on the previous two steps,the translocation process for *** in the suitable habitat will be done after the simple layering process as one of the most effective traditional vegetative methods for wild cultivation for this *** steps aimed to reduce the impact of threats and the risk of extinction through increasing the population size,the Extent of Occurrence(EOO),and the Area of Occupancy(AOO).We extracted the environmental factors controlling the target species’distribution and habitat suitability range using the IUCN Red List assessment and species distribution Model(SDM).The most suitable habitat for *** is predicted in the middle northern parts of SCPA,with the highest suitability in the High *** of driest quarter,precipitation of wettest month,precipitation of coldest quarter,and aspect are the highest mean contributors determining the distribution of *** in *** arabica potential distribution covers 324.4 km^(2)(7.46%)of the total SCPA area(4350 km^(2)).This area is divided into:18.1 km^(2)high

关键词： Mountain ecosystems Recovery Translocations Layering Conservation species distribution modelling Maxent IUCN Red List

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Detecting patterns of climate change in long-term forecasts of marine environmental parameters

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International Journal of Digital Earth 2020年第5期13卷 567-585页

作者： Gianpaolo Coro Pasquale Pagano Anton Ellenbroek Istituto di Scienza e Tecnologie dell’Informazione“A.Faedo”–CNR PisaItaly b Food and Agriculture Organization of the United Nations(FAO) RomeItaly

Forecasting environmental parameters in the distant future requires complex modelling and large computational *** to the sensitivity and complexity of forecast models,long-term parameter forecasts(*** to 2100)are uncommon and only produced by a few organisations,in heterogeneous formats and based on different assumptions of greenhouse gases ***,data mining techniques can be used to coerce the data to a uniform time and spatial representation,which facilitates their use in many *** this paper,streams of big data coming from AquaMaps and NASA collections of 126 long-term forecasts of nine types of environmental parameters are processed through a cloud computing platform in order to(i)standardise and harmonise the data representations,(ii)produce intermediate scenarios and new informative parameters,and(iii)align all sets on a common time and spatial *** series crosscorrelation applied to these aligned datasets reveals patterns of climate change and similarities between parameter trends in 10 marine *** results highlight that(i)the Mediterranean Sea may have a standalone‘response’to climate change with respect to other areas,(ii)the Poles are most representative of global forecasted change,and(iii)the trends are generally alarming for most oceans.

关键词： Climate change environmental parameters forecasting environmental parameters time series ecological modelling species distribution modelling AquaMaps NASA Earth Exchange

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Spatial distribution modelling of Kobresia pygmaea(Cyperaceae) on the Qinghai-Tibetan Plateau

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Journal of Resources and Ecology 2017年第1期8卷 20-29页

作者： WU Liang WANG Min OUYANG Hua CHENG Shengkui SONG MingHua Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China 100101 Policy Research Center for Environment and Economy Ministry of Environmental Protection Beijing China 100029

Kobresia pygmaea *** the alpine meadow ecosystem on the Qinghai-Tibet *** of this species＇ distribution and ecological environment could provide valuable insights into the alpine ecosystem and key species living there,support species and ecosystem conservation in alpine regions,and build on species origin and evolutionary *** avoid modelling uncertainty encountered in a single approach,four species distribution model algorithms（Surface Range Envelope（SRE）,Generalized Linear Model（GLM）,Generalized Boosted Regression（GBM） and Maximum Entropy（MAXENT））,were used to simulate the distribution of *** based on occurrence samples that were verified using DNA sequencing *** distribution modelling revealed a vast distribution region of *** in the northern Tibetan Highlands and alpine meadows in the southern and eastern declivity of the plateau.A high evaluation performance was found for the GLM,GBM and MAXENT *** potential range size patterns for the four models were found between 374340–482605 km^2（average = 421591 km^2）.Precipitation during growing seasons was found to be the dominant factor accounting for the distribution,consistent with patterns of heat and water patterns conditions of alpine ecosystems on the *** distribution models provide a simple and reliable approach to simulating the spatial patterns of species inhabiting the Qinghai-Tibet Plateau.

关键词： species distribution modelling Qinghai-Tibet Plateau Kobresia pygmaea Willd

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Study of Vulture Habitat Suitability and Impact of Climate Change in Central India Using MaxEnt

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Journal of Resources and Ecology 2021年第1期12卷 30-42页

作者： Kaushalendra K.JHA Radhika JHA Indian Institute of Forest Management Nehru NagarBhopalMadhya Pradesh 462003India Department of Zoology University of LucknowLucknowUttar Pradesh 226007India

Vultures provide invaluable ecosystem services and play an important role in ecosystem balancing. The number of native vultures in India has declined in the past. Acquiring present knowledge of their habitat spread is essential to manage and prevent such a decline. It is envisaged that ongoing climate crisis may further cause change in habitat suitability and impact the existing population. Therefore, this study in Central India—a vulture stronghold, is aimed at predicting habitat changes in the short and long term and present the data statistically and graphically by using species distribution Model. MaxEnt software was chosen for its advantages over other models, like using presence-only data and performing well with incomplete data, small sample sizes and gaps, etc. Global Climate Model ensemble(CCSM4, HadGEM2 AO and MIROC5), was used to get better prediction. Fourteen robust models(AUC 0.864 0.892) were developed using data from over 1000 locations of seven vulture species over two seasons together. Selected climatic and other environmental variables were used to predict the current habitat. Future prediction was based on climatic variables only. The most important variables influencing the distribution were precipitation(bio 15, bio 18, bio 19) and temperature(bio 3, bio 5). Forest and water bodies were the major influencers within land use-landcover in the current prediction. At finer scale, while extremely suitable habitat area decreased and highly suitable area increased over time, the total suitable area marginally increased in 2050 but decreased in 2070. For broader consideration, net loss in suitable area was 5% in 2050 and 7.17% in 2070(RCP4.5). Similarly, in the RCP8.5 this was 6% in 2050 and 7.3% in 2070. The data generated can be used in conservation planning and management and thus protecting the vultures from any future threat.

关键词： ensemble climate model Indian vulture-stronghold long term impact short term impact species distribution modelling vulture habitats

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