Erschienen in:

11.01.2023 | Original Paper

Predicting habitat suitability of Litsea glutinosa: a declining tree species, under the current and future climate change scenarios in India

verfasst von: Rajit Gupta, Laxmi Kant Sharma, Muthu Rajkumar, Naseer Mohammad, Mohammed Latif Khan

Erschienen in: Landscape and Ecological Engineering | Ausgabe 2/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Die Studie verwendet das MaxEnt-Modell, um die Habitateignung von Litsea glutinosa, einer vom Aussterben bedrohten Baumart in Indien, unter aktuellen und zukünftigen Szenarien des Klimawandels vorherzusagen. Durch die Analyse bioklimatischer und topografischer Variablen identifiziert die Forschung Schlüsselfaktoren, die die Verbreitung der Arten beeinflussen, und projiziert Veränderungen der Habitateignung über verschiedene Emissionsszenarien hinweg. Die Ergebnisse sind von entscheidender Bedeutung für die Entwicklung wirksamer Strategien zum Schutz dieser wertvollen Art angesichts des Klimawandels und des vom Menschen ausgehenden Drucks.

KI-Generiert

Diese Zusammenfassung des Fachinhalts wurde mit Hilfe von KI generiert.

Vorheriger Artikel To see what we need: recognizing ecosystem services in a campus landscape through environmental education

Nächster Artikel The 30-year impact of post-windthrow management on the forest regeneration process in northern Japan

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Abdullah AYM, Dewan A, Shogib MRI, Rahman MM, Hossain MF (2017) Environmental factors associated with the distribution of Visceral leishmaniasis in endemic areas of Bangladesh: modeling the ecological niche. Trop Med Health 45:1–15. https://doi.org/10.1186/s41182-017-0054-9CrossRef

Austin MP, Van Niel KP (2011) Improving species distribution models for climate change studies: variable selection and scale. J Biogeogr 38:1–8. https://doi.org/10.1111/j.1365-2699.2010.02416.xCrossRef

Beaumont LJ, Hughes L, Poulsen M (2005) Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecol Model 186:251–270. https://doi.org/10.1016/j.ecolmodel.2005.01.030CrossRef

Chitale VS, Behera MD, Roy PS (2014) Future of endemic flora of biodiversity hotspots in India. PLoS ONE 12:e115264. https://doi.org/10.1371/journal.pone.0115264CrossRef

Dev S (2006) A selection of prime ayurvedic plant drugs: ancient-modern concordance. Anmaya Publishers, New Delhi

Du Z, He Y, Wang H, Wang C, Duan Y (2021) Potential geographical distribution and habitat shift of the genus Ammopiptanthus in China under current and future climate change based on the MaxEnt model. J Arid Environ 184:104328. https://doi.org/10.1016/j.jaridenv.2020.104328CrossRef

Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.xCrossRef

FAO (2010) Global forest resources assessment 2010: main report. Food and Agriculture Organization of the United Nations, Rome

Fick SE, Hijmans RJ (2017) WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. Int J Climatol 37:4302–4315. https://doi.org/10.1002/joc.5086CrossRef

Freeman BG, Scholer MN, Ruiz-Gutierrez V, Fitzpatrick JW (2018) Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community. PNAS 115:11982–11987. https://doi.org/10.1073/pnas.1804224115CrossRefPubMedPubMedCentral

GBIF: The Global Biodiversity Information Facility (year) What is GBIF?. https://www.gbif.org/what-is-gbif. Accessed 25 Jan 2022

Gebrewahid Y, Abrehe S, Meresa E, Eyasu G, Abay K, Gebreab G, Kidanemariam K, Adissu G, Abreha G, Darcha G (2020) Current and future predicting potential areas of Oxytenanthera abyssinica (A. Richard) using MaxEnt model under climate change in Northern Ethiopia. Ecol Process 9:1–15. https://doi.org/10.1186/s13717-019-0210-8CrossRef

Gilani H, Goheer MA, Ahmad H, Hussain K (2020) Under predicted climate change: Distribution and ecological niche modelling of six native tree species in Gilgit-Baltistan, Pakistan. Ecol Indic 111:106049. https://doi.org/10.1016/j.ecolind.2019.106049CrossRef

Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009. https://doi.org/10.1111/j.1461-0248.2005.00792.xCrossRefPubMed

Guisan A, Edwards TC Jr, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model 157(2–3):89–100CrossRef

Herbarium JCB http://flora-peninsula-indica.ces.iisc.ac.in/index.php. Accessed 28 Jan 2022

World Bank Climate Change Knowledge Portal https://climateknowledgeportal.worldbank.org/country/india/climate-data-historical. Accessed 20 Feb 2022

India Biodiversity Portal https://indiabiodiversity.org/. Accessed 26 Jan 2022

WorldClim https://worldclim.org/. Accessed 05 Feb 2022

IPCC (2021) Climate change 2021: the physical science basis. In: Masson-Delmotte V, Zhai P, Pirani A, Connors SL, Péan C, Berger S, Caud N, Chen Y, Goldfarb L, Gomis MI, Huang M, Leitzell K, Lonnoy E, Matthews JBR, Maycock TK, Waterfield T, Yelekçi O, Yu R, Zhou B (eds) Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

ISFR (2021) Dehradun: Ministry of Environment and Forests, Forest Survey of India (FSI)

John C, Post E (2022) Projected bioclimatic distributions in Nearctic Bovidae signal the potential for reduced overlap with protected areas. Ecol Evol 12(8):e9189. https://doi.org/10.1002/ece3.9189CrossRefPubMedPubMedCentral

Kaky E, Nolan V, Alatawi A, Gilbert F (2020) A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants. Ecol Inform 60:101150. https://doi.org/10.1016/j.ecoinf.2020.101150CrossRef

Li J, Chang H, Liu T, Zhang C (2019) The potential geographical distribution of Haloxylon across Central Asia under climate change in the 21st century. Agric for Meteorol 275:243–254. https://doi.org/10.1016/j.agrformet.2019.05.02710.1016/j.agrformet.2019.05.027CrossRef

Li J, Fan G, He Y (2020) Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis. Sci Total Environ 698:134141CrossRefPubMed

Liu D, Lei X, Gao W, Guo H, Xie Y, Fu L, Lei Y, Li Y, Zhang Z, Tang S (2022) Mapping the potential distribution suitability of 16 tree species under climate change in northeastern China using Maxent modelling. J for Res. https://doi.org/10.1007/s11676-022-01459-4CrossRef

Low BW, Zeng Y, Tan HH, Yeo DC (2021) Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species. Divers Distrib 27:497–511. https://doi.org/10.1111/ddi.13211CrossRef

Ma B, Sun J (2018) Predicting the distribution of Stipa purpurea across the Tibetan Plateau via the MaxEnt model. BMC Ecol 18:1–12. https://doi.org/10.1186/s12898-018-0165-0CrossRef

Märkel U, Dolos K (2017) Tree species site suitability as a combination of occurrence probability and growth and derivation of priority regions for climate change adaptation. Forests 8(6):181. https://doi.org/10.3390/f8060181CrossRef

Marquardt DW (1970) Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics 12:591–612. https://doi.org/10.2307/1267205CrossRef

Martinez-Austria PF, Bandala ER, Patiño-Gómez C (2016) Temperature and heat wave trends in northwest Mexico. Phys Chem Earth Parts a/b/c 91:20–26. https://doi.org/10.1016/j.pce.2015.07.005CrossRef

McKenney DW, Pedlar JH, Lawrence K, Campbell K, Hutchinson MF (2007) Potential impacts of climate change on the distribution of North American trees. Bioscience 57(11):939–948. https://doi.org/10.1641/B571106CrossRef

Merow C, Smith MJ, Silander JA (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36:1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.xCrossRef

Mishra SK, Kumar A, Talukdar A (2010) Evaluation of binding property of mucilage from Litsea glutinosa Wall. Phcog Res 2:289–292. https://doi.org/10.4103/0974-8490.72325CrossRefPubMedPubMedCentral

Mohammad N, Dahayat A, Yadav M, Shirin F, Ansari SA (2018) Genetic diversity and population structure of Litsea glutinosa (Lour.) in Central India. Physiol Mol Biol Plants 24(4):655–663. https://doi.org/10.1007/s12298-018-0556-xCrossRefPubMedPubMedCentral

Na X, Zhou H, Zang S, Wu C, Li W, Li M (2018) Maximum Entropy modeling for habitat suitability assessment of Red-crowned crane. Ecol Indicat 91:439–446. https://doi.org/10.1016/j.ecolind.2018.04.013CrossRef

Naimi B, Araújo MB (2016) sdm: a reproducible and extensible R platform for species distribution modelling. Ecography 39(4):368–375. https://doi.org/10.1111/ecog.01881CrossRef

O’Donnell MS, Ignizio DA (2012) Bioclimatic predictors for supporting ecological applications in the conterminous United States: U.S. Geol Surv Data Ser 691:10

Padalia H, Srivastava V, Kushwaha SPS (2014) Modeling potential invasion range of alien invasive species, Hyptis suaveolens (L.) Poit. in India: comparison of MaxEnt and GARP. Ecol Infor 22:36–43. https://doi.org/10.1016/j.ecoinf.2014.04.002CrossRef

Pandey AK, Mandal AK (2012) Sustainable Harvesting of Terminalia arjuna (Roxb.) Wight & Arnot (Arjuna) and Litsea glutinosa (Lour.) Robinson (Maida) Bark in Central India. J Sustain for 31:294–309. https://doi.org/10.1080/10549811.2011.583865CrossRef

Pearce JL, Boyce MS (2006) Modelling distribution and abundance with presence-only data. J Appl Ecol 43:405–412. https://doi.org/10.1111/j.1365-2664.2005.01112.xCrossRef

Pearson RG, Raxworthy CJ, Nakamura M, Peterson AT (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34:102–117. https://doi.org/10.1111/j.1365-2699.2006.01594.xCrossRef

Peterson TA, Papeş M, Eaton M (2007) Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. Ecography 30:550–560. https://doi.org/10.1111/j.0906-7590.2007.05102.xCrossRef

Peterson AT et al (2011) Ecological niches and geographic distributions: e-book. Princeton University Press, PrincetonCrossRef

Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.xCrossRef

Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Modell 190:231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026CrossRef

Phillips SJ, Dudík M, Schapire RE (2004) A maximum entropy approach to species distribution modeling. In: Proceedings of the Twenty-First International Conference on Machine Learning. ACM, 83.

Pramanik M, Paudel U, Mondal B, Chakraborti S, Deb P (2018) Predicting climate change impacts on the distribution of the threatened Garcinia indica in the Western Ghats, India. Clim Risk Manag 19:94–105. https://doi.org/10.1016/j.crm.2017.11.002CrossRef

R Core Team (2021) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria

Radosavljevic A, Anderson RP, Araújo M (2014) Making better Maxentmodels of species distributions: complexity, overfitting and evaluation. J Biogeogr 41:629–643. https://doi.org/10.1111/jbi.12227CrossRef

Rajashekar G, Fararoda R, Reddy RS, Jha CS, Ganeshaiah KN, Singh JS, Dadhwal VK (2018) Spatial distribution of forest biomass carbon (above and below ground) in Indian forests. Ecol Indic 85:742–752. https://doi.org/10.1016/j.ecolind.2017.11.024CrossRef

Ramana KV, Raju AS (2017) Traditional and commercial uses of Litsea glutinosa (Lour.) C.B. Robinson (Lauraceae). J Med Plants Stud 5:89–91

Reddy KN, Reddy CS (2008) First red-list of medicinal plants of Andhra Pradesh, India. Conservation Assessment and Management Planning. Ethnobot Leaflets 12:103–107

Reddy CS, Jha CS, Diwakar PG, Dadhwal VK (2015) Nationwide classification of forest types of India using remote sensing and GIS. Environ Monit Assess 187(12):1–30. https://doi.org/10.1007/s10661-015-4990-8CrossRef

Remya K, Ramachandran A, Jayakumar AS (2015) Predicting the current and future suitable habitat distribution of Myristica dactyloides Gaertn. using MaxEnt model in the Eastern Ghats. India Ecol Eng 82:184–188. https://doi.org/10.1016/j.ecoleng.2015.04.053CrossRef

Riahi K, Van Vuuren DP, Kriegler E, Edmonds J, O’neill BC, Fujimori S, Bauer N, Calvin K, Dellink R, Fricko O, Lutz W et al (2017) The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob Environ Change 42:153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009CrossRef

RStudio Team (2020) RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/

Saputra MH, Lee HS (2021) Evaluation of climate change impacts on the potential distribution of Styraxsumatrana in North Sumatra, Indonesia. Sustainability 13:462. https://doi.org/10.3390/su13020462CrossRef

Sharma LK, Gupta R, Fatima N (2022) Assessing the predictive efficacy of six machine learning algorithms for the susceptibility of Indian forests to fire. Int J Wildland Fire 31:735–758. https://doi.org/10.1071/WF22016CrossRef

Skeie RB, Myhre G, Hodnebrog Ø, Cameron-Smith PJ, Deushi M, Hegglin MI, Horowitz LW, Kramer RJ, Michou M, Mills MJ et al (2020) Historical total ozone radiative forcing derived from CMIP6 simulations. NPJ Clim Atmos Sci 3:32. https://doi.org/10.1038/s41612-020-00131-0CrossRef

Srinivas SG, Krishnamurthy YL (2019) Altitude and ecological distribution of genus litsea (Lauraceae) in Western Ghats of Karnataka, India. J Trop for Sci. https://doi.org/10.31357/jtfe.v9i2.4473CrossRef

Tang X, Yuan Y, Li X, Zhang J (2021) Maximum entropy modeling to predict the impact of climate change on pine wilt disease in China. Front Plant Sci 12:764. https://doi.org/10.3389/fpls.2021.652500CrossRef

Tatebe H, Ogura T, Nitta T, Komuro Y et al (2019) Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geosci Model Dev 12:2727–2765. https://doi.org/10.5194/gmd-12-2727-2019CrossRef

Tebaldi C, Debeire K, Eyring V, Fischer E, Fyfe J, Friedlingstein P, Knutti R, Lowe J, O’Neill B, Sanderson B, Van Vuuren D (2021) Climate model projections from the scenario model intercomparison project (ScenarioMIP) of CMIP6. Earth Syst Dyn 12:253–293. https://doi.org/10.5194/esd-12-253-2021CrossRef

Thapa S, Chitale V, Rijal SJ, Bisht N, Shrestha BB (2018) Understanding the dynamics in distribution of invasive alien plant species under predicted climate change in Western Himalaya. PLoS ONE 13(4):e0195752. https://doi.org/10.1371/journal.pone.0195752CrossRefPubMedPubMedCentral

Velásquez-Tibatá J, Salaman P, Graham CH (2013) Effects of climate change on species distribution, community structure, and conservation of birds in protected areas in Colombia. Reg Environ Change 13:235–248. https://doi.org/10.1007/s10113-012-0329-yCrossRef

Vos P (2008) Case studies on the status of invasive woody plant species in the western Indian Ocean. 2. The Comoros Archipelago (Union of the Comoros and Mayotte). Food and Agriculture Organization of the United Nations, Forestry Department, Forest Resources Division, Forest Resources Development Service, Working Paper FBS/4-2E., 34

Wang R, Li Q, He S, Liu Y, Wang M, Jiang G (2018) Modeling and mapping the current and future distribution of Pseudomonas syringae pv. actinidiae under climate change in China. PLoS ONE 13(2):e0192153. https://doi.org/10.1371/journal.pone.0192153CrossRefPubMedPubMedCentral

Warton DI, Renner IW, Ramp D (2013) Model-based control of observer bias for the analysis of presence-only data in ecology. PLoS ONE. https://doi.org/10.1371/journal.pone.0079168CrossRefPubMedPubMedCentral

Wei B, Wang R, Hou K, Wang X, Wu W (2018) Predicting the current and future cultivation regions of Carthamus tinctorius L. using MaxEnt model under climate change in China. Glob Ecol Conserv 16:e00477. https://doi.org/10.1016/j.gecco.2018.e00477CrossRef

Wintle BA, Bardos DC (2006) Modeling species habitat relationships with spatially auto-correlated observation data. Ecol Appl 16:1945–1958. https://doi.org/10.1890/1051-0761(2006)016[1945:msrwsa]2.0.co;2CrossRefPubMed

Xu D, Zhuo Z, Wang R, Ye M, Pu B (2019) Modeling the distribution of Zanthoxylum armatum in China with MaxEnt modeling. Glob Ecol Conserv 19:e00691. https://doi.org/10.1016/j.gecco.2019.e00691CrossRef

Yang XQ, Kushwaha SPS, Saran S, Xu J, Roy PS (2013) Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecol Eng 51:83–87. https://doi.org/10.1016/j.ecoleng.2012.12.004CrossRef

Yi YJ, Cheng X, Yang ZF, Zhang SH (2016) Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China. Ecol Eng 92:260–269. https://doi.org/10.1016/j.ecoleng.2016.04.010CrossRef

Zhang K, Yao L, Meng J, Tao J (2018) Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Sci Total Environ 634:1326–1334. https://doi.org/10.1016/j.scitotenv.2018.04.112CrossRefPubMed

Zhang J, Jiang F, Li G, Qin W, Li S, Gao H, Cai Z, Lin G, Zhang T (2019a) Maxent modeling for predicting the spatial distribution of three raptors in the Sanjiangyuan National Park. China Ecol Evol 9(11):6643–6654. https://doi.org/10.1002/ece3.5243CrossRefPubMed

Zhang K, Zhang Y, Tao J (2019b) Predicting the potential distribution of Paeonia veitchii (Paeoniaceae) in China by incorporating climate change into a Maxent model. Forests 10:190. https://doi.org/10.3390/f10020190CrossRef

Zhang K, Zhang Y, Zhou C, Meng J, Sun J, Zhou T, Tao J (2019c) Impact of climate factors on future distributions of Paeonia ostii across China estimated by MaxEnt. Ecol Inform 50:62–67. https://doi.org/10.1016/j.ecoinf.2019.01.004CrossRef

Zhang K, Sun L, Tao J (2020) Impact of climate change on the distribution of Euscaphis japonica (Staphyleaceae) trees. Forests 11:525. https://doi.org/10.3390/f11050525CrossRef

Zhang Y, Tang J, Ren G, Zhao K, Wang X (2021) Global potential distribution prediction of Xanthium italicum based on Maxent model. Sci Rep 11:1–10. https://doi.org/10.1038/s41598-021-96041-zCrossRef

Zhou Y, Zhang Z, Zhu B, Cheng X, Yang L, Gao M, Kong R (2021) MaxEnt Modeling Based on CMIP6 Models to Project Potential Suitable Zones for Cunninghamia lanceolata in China. Forests 12:752. https://doi.org/10.3390/f12060752CrossRef

Titel: Predicting habitat suitability of Litsea glutinosa: a declining tree species, under the current and future climate change scenarios in India
verfasst von: Rajit Gupta
Laxmi Kant Sharma
Muthu Rajkumar
Naseer Mohammad
Mohammed Latif Khan
Publikationsdatum: 11.01.2023
Verlag: Springer Japan
Erschienen in: Landscape and Ecological Engineering / Ausgabe 2/2023
Print ISSN: 1860-1871
Elektronische ISSN: 1860-188X
DOI: https://doi.org/10.1007/s11355-023-00537-x

Mapping local knowledge through spatial text mining

Original Paper

The 30-year impact of post-windthrow management on the forest regeneration process in northern Japan

Open Access
Original Paper

Investigation of hydrodynamics along an embankment generated by a nearby riparian vegetation patch

Original Paper

Evaluation of pre- and post-fire flood risk by analytical hierarchy process method: a case study for the 2021 wildfires in Bodrum, Turkey

Original Paper

To see what we need: recognizing ecosystem services in a campus landscape through environmental education

Original Paper

Responsive evaluation of college students-in-residence rural community competition

Special Feature: Original Paper

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Mapping local knowledge through spatial text mining

The 30-year impact of post-windthrow management on the forest regeneration process in northern Japan

Investigation of hydrodynamics along an embankment generated by a nearby riparian vegetation patch

Evaluation of pre- and post-fire flood risk by analytical hierarchy process method: a case study for the 2021 wildfires in Bodrum, Turkey

To see what we need: recognizing ecosystem services in a campus landscape through environmental education

Responsive evaluation of college students-in-residence rural community competition