PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2017 | 65 | 1 |
Tytuł artykułu

The impact of climate change on habitat suitability for Artemisia sieberi and Artemisia aucheri (Asteraceae) – a modeling approach

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We determined the current potential distribution of Artemisia sieberi and A. aucheri, two important widespread rangeland shrub species in Iran, using bioclimatic variables with and without the addition of elevation (E) to the MaxEnt model. The impact of climate change on the habitat suitability of the Artemisiaspecies was modeled for mid century under the projected climate change of GFDL-ESM2G (RCP2.6) model, a warmer and slightly wetter condition, and CCSM4 (RCP4.5) model, a warmer and drier condition. The results showed that annual precipitation (AP) and temperature annual range (TAR) were the most important drivers of A. aucheri distribution at a regional scale. With the addition of E to the model, we found that E and AP were the most significant factors in determining the habitat suitability of this species. The most significant factors influencing A. sieberi distribution were AP and annual mean temperature (AMT). E was not identified as the important variable influencing A. sieberi distribution when was added to the model in spite of its high correlation to AMT (|r| > 0.8), while AP was the most important, indicating that A. sieberi is less dependent on elevation than A. aucheri. A. aucheri is regarded as a high elevation species (E > 2500 m) which can be distributed in colder and wetter areas as compared to A. sieberi, a mid-elevation species (E < 2500 m). The projected climate change using both models has a much more impact on A. aucheri, potentially driving more losses and fewer gains in climatically suitable habitat of this species as compared to A. sieberi suggesting the adaptation of the later to a wider range of climatic conditions than A. aucheri. The results of the current and future distribution modeling of the Artemisia species is significant in managing susceptible habitats of these species for climate change and for habitat restoration.
Wydawca
-
Rocznik
Tom
65
Numer
1
Opis fizyczny
p.97-109,fig.,ref.
Twórcy
  • Department of Plant Protection, Yazd Branch, Islamic Azad University, Yazd, Iran
autor
  • Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA 90095, USA
Bibliografia
  • Al-Qaddi N., Vessella F., Stephan J., Al-Eisawi D., and Schirone B. 2016 — Current and future suitability areas of kermes oak (Quercus coccifera L.) in the Levant under climate change — Regional Environ. Change, 1–14.
  • Banag C., Thrippleton T., Alejandro G.J., Reineking B., Liede-Schumann S. 2015 — Bioclimatic niches of selected endemic Ixora species on the Philippines: predicting habitat suitability due to climate change — Plant Ecol. 216: 1325–1340.
  • Brummer T.J., Taylor K.T., Rotella J., Maxwell B.D., Rew L.J., Lavin M. 2016 — Drivers of Bromus tectorumabundance in the Western North American sagebrush steppe — Ecosystems, 19: 986–1000.
  • DeSimone S.A., Burk J.H. 1992 — Local variation in floristics and distributional factors in Californian coastal sage scrub — Madroño, 39: 170–188.
  • Golicher D.J., Cayuela L., Newton A.C. 2012 — Effects of climate change on the potential species richness of Mesoamerican forests — Biotropica, 44: 284–293.
  • Guisan A., Weiss S.B., Weiss A.D. 1999 — GLM versus CCA spatial modeling of plant species distribution — Plant Ecol. 143: 107–122.
  • Guisan A., Zimmermann N.E. 2000 — Predictive habitat distribution models in ecology —Ecol. Model. 135: 147–186.
  • Henderson E.B., Ohmann J.L., Gregory M.J., Roberts H.M., Zald H. 2014 — Species distribution modelling for plant communities: stacked single species or multivariate modelling approaches? — Appl. Veg. Sci. 17: 516–527.
  • Hosseini S.Z., Kappas M., Zare Chahouki M.A., Gerold G., Erasmi S., Rafiei Emam A. 2013 — Modelling potential habitats for Artemisia sieberi and Artemisia aucheri in Poshtkouh area, central Iran using the maximum entropy model and geostatistics — Ecol. Inform. 18: 61–68.
  • IPCC, Stocker T.F., Qin D., Plattner G.K., Tignor M., Allen S.K., Boschung J., Nauels A., Xia Y., Bex B., Midgley B.M. 2013 —. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
  • Ishida S., Yamazaki A., Takanose Y., Kamitani T. 2010 — Off-channel temporary pools contribute to native riparian plant species diversity in a regulated river floodplain — Ecol. Res. 25: 1045–1055.
  • Kearney M.R., Wintle B.A., Porter W.P. 2010 — Correlative and mechanistic models of species distribution provide congruent forecasts under climate change — Conserv. Lett. 3: 203–213.
  • Kleidon A., Mooney H.A. 2000 — A global distribution of biodiversity inferred from climatic constraints: results from a process based modelling study —. Glob. Change Biol. 6: 507–523.
  • Kousari M.R., Ekhtesasi M.R., Tazeh M., Naeini M.A.S., Zarch M.A.A. 2011 — An investigation of the Iranian climatic changes by considering the precipitation, temperature, and relative humidity parameters — Theor. Appl. Climatol. 103: 321–335.
  • Kousari M.R., Zarch M.A.A. 2011 — Minimum, maximum, and mean annual temperatures, relative humidity, and precipitation trends in arid and semi-arid regions of Iran — Arab. J. Geosci. 4: 907–914.
  • Liu C., Berry P.M., Dawson T.P., Pearson R.G. 2005 — Selecting thresholds of occurrence in the prediction of species distributions — Ecography, 28: 385–393.
  • Loarie S.R., Carter B.E., Hayhoe K., McMahon S., Moe R. 2008 —. Climate change and the future of California's endemic flora —. PLoS ONE, 3: e2502.
  • Loiselle B.A., Jørgensen P.M., Consiglio T., Jimenez I., Blake J.G., Lohmann L.G., Montiel O.M. 2008 — Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? — J. Biogeogr. 35: 105–116.
  • Luoto M., Virkkala R., Heikkinen R.K. 2007 — The role of land cover in bioclimatic models depends on spatial resolution — Global Ecology and Biogeography, 16: 34–42.
  • Manish K., Telwala Y., Nautiyal D.C., Pandit M.K. 2016 — Modelling the impacts of future climate change on plant communities in the Himalaya: a case study from Eastern Himalaya, India —. Model. Earth Syst. Environ. 2: 1–12.
  • Manthey M., Box E.O. 2007 — Realized climatic niches of deciduous trees: comparing western Eurasia and eastern North America — J. Biogeogr. 34:1028–1040.
  • McKenney D.W., Pedlar J.H., Lawrence K., Campbell K., Hutchinson, M.F. 2007 — Beyond traditional hardiness zones: using climate envelopes to map plant range limits — BioScience, 57: 929–937.
  • Meehl G.A., Washington W.M., Arblaster J.M., Hu A., Teng H., Tebaldi C., Sanderson B.N., Lamarque J.F., Conley A., Strand W.G. White III J.B. 2012 — Climate system response to external forcing and climate change projections in CCSM4 — J. Climate, 25: 3661–3683.
  • Merow C., Smith M.J., Silander J.A. 2013 — A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter — Ecography, 36: 1058–1069.
  • Mousaei Sanjerehei M. 2014 — Determination of the probability of the occurrence of Iran life zones (an integration of binary logistic regression and geostatistics) — J. Biodiv. Environ. Sci. 4: 408–417.
  • Ohmann J.L., Gregory M.J. 2002 — Predictive mapping of forest composition and structure with direct gradient analysis and nearest-neighbor imputation in coastal Oregon, USA —. Can. J. Forest Res. 32: 725–741.
  • Ohse B., Huettmann F., Ickert-Bond S.M., Juday G.P. 2009 — Modeling the distribution of white spruce (Picea glauca) for Alaska with high accuracy: an open access role-model for predicting tree species in last remaining wilderness areas — Polar biol. 32: 1717–1729.
  • Parmesan C. 2006 — Ecological and evolutionary responses to recent climate change — Annu. Rev. Ecol. Evol. Syst. 37: 637–669.
  • Pearson R.G., Dawson T.P. 2003 — Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? —. Global Ecol. Biogeog. 12: 361–371.
  • Pearson R.G., Raxworthy C.J., Nakamura M., Peterson A.T. 2007 — Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar — J. Biogeogr. 34: 102–117.
  • Phillips S.J., Anderson R.P., Schapire R.E. 2006 — Maximum entropy modeling of species geographic distributions — Ecol. model. 190: 231–259.
  • Phillips S.J., Dudik M. 2008 — Modeling of species distributions with MaxEnt: new extensions and a comprehensive evaluation — Ecography, 31: 161–175.
  • Rabiei M., Asri Y., Hamzehee B., Jalili A. 2010 — Flora of Artemisia sieberi habitat in Iran — Iran. J. Bot. 22: 645–660.
  • Riordan E.C., Rundel P.W. 2009 — Modelling the distribution of a threatened habitat: the California sage scrub — J. Biogeogr. 36: 2176–2188.
  • Riordan E.C., Rundel P.W. 2014—Land use compounds habitat losses under projected climate change in a threatened California ecosystem —. PloS ONE, 9: 1–15.
  • Still S.M., Richardson B.A. 2015 — Projections of contemporary and future climate niche for Wyoming big sagebrush (Artemisia tridentata subsp. wyomingensis): a guide for restoration —. Nat. Areas J. 35: 30–43.
  • Van de Rijt C.W.C.J., Hazelhoff L., Blom C.W.P.M. 1996 — Vegetation zonation in a former tidal area: a vegetation-type response model based on DCA and logistic regression using GIS — J. Veg. Sci. 7: 505–518.
  • Wisz M.S., Hijmans R.J., Li J., Peterson A.T., Graham C.H., Guisan A. 2008 — Effects of sample size on the performance of species distribution models — Div. Distrib. 14: 763–773.
  • Wohlgemuth T., Nobis M.P., Kienast F., Plattner M. 2008 — Modelling vascular plant diversity at the landscape scale using systematic samples — J. Biogeogr. 35: 1226–1240.
  • Yaghmaei L., Soltani Koupaei S., Khoda gholami M. 2008 — Effect of climatic factors on distribution of Artemisia sieberi and Artemisia aucheri in isfahan province using multivariate statistical methods — Water and Soil Science, 12: 359–371.
  • Zhou M., Wang H. 2015 — Potential impact of future climate change on crop yield in northeastern China —. Adv. Atmosph Sci. 32: 889–897.
  • Zomer R.J., Trabucco A., Wang M., Lang R., Chen H., Metzger M.J., Smajgl A., Beckschäfer P., Xu J. 2014 — Environmental stratification to model climate change impacts on biodiversity and rubber production in Xishuangbanna, Yunnan, China — Biol. Conserv. 170: 264–273.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.agro-0a4ddb70-a480-4322-b904-22df4279f58b
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.