An improved species distribution model for Scots pine and downy oak under future climate change in the NW Italian Alps

Climate is the main environmental driver determining the spatial distribution of most tree species at the continental scale. We investigated the distribution change of European beech and Norway spruce due to climate change. We applied a species distribution model (SDM), driven by an ensemble of 21 regional climate models in order to study the shift of the favourability distribution of these species. SDMs were parameterized for 1971–2000, as well as 2021–2050 and 2071–2100 using the SRES scenario A1B and three physiological meaningful climate variables. Growing degree sum and precipitation sum were calculated for the growing season on a basis of daily data. Results show a general north-eastern and altitudinal shift in climatological favourability for both species, although the shift is more marked for spruce. The gain of new favourable sites in the north or in the Alps is stronger for beech compared to spruce. Uncertainty is expressed as the variance of the averaged maps and with a density function. Uncertainty in species distribution increases over time. This study demonstrates the importance of data ensembles and shows how to deal with different outcomes in order to improve impact studies by showing uncertainty of the resulting maps.

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Projecting Suitability and Climate Vulnerability of Bhutanitis thaidina (Blanchard) (Lepidoptera: Papilionidae) with Conservation Implications

Scientific Reports ◽

10.1038/s41598-019-51972-6 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Shao-Ji Hu ◽

Dong-Hui Xing ◽

Zhi-Xian Gong ◽

Jin-Ming Hu

Keyword(s):

Climate Change ◽

Habitat Fragmentation ◽

Species Distribution Model ◽

Population Decline ◽

Future Climate ◽

Food Plants ◽

Distribution Model ◽

The Impact ◽

Northwestern Yunnan ◽

Northeastern Yunnan

Abstract Bhutanitis thaidina is an endemic, rare, and protected swallowtail in China. Deforestation, habitat fragmentation, illegal commercialised capture, and exploitation of larval food plants are believed to be the four major causes of population decline of B. thaidina in the recent decade. However, little attention was paid to the impact of climate change. This study used ecological niche factor analysis and species distribution model to analyse the current suitable areas for B. thaidina with BioClim variables as well as its future suitable areas under four future climate scenarios (represented by four Representative Concentration Pathways: RCP2.6, RCP4.5, RCP6.0, and RCP8.5). Statistical analysis was carried out to compare the possible area and altitude changes to the distribution of B. thaidina under changing climate. Our analyses showed that the suitable areas for B. thaidina are fragmented under the current climate, with four suitable centres in northwestern Yunnan, northeastern Yunnan and northwestern Guizhou, the western margin of Sichuan Basin, and Qinling mountains. Apart from further habitat fragmentation under climate change, slight range expansion (average 6.0–8.9%) was detected under the RCP2.6 and RCP4.5 scenarios, while more range contraction (average 1.3–26.9%) was detected under the RCP6.0 and RCP8.5 scenarios, with the two southern suitable centres suffering most. Also, a tendency of contraction (2,500–3,500 m) and upslope shift (~600 m) in suitable altitude range were detected. The findings of this study supported the climate-vulnerable hypothesis of B. thaidina, especially under future climate like the RCP6.0 and RCP8.5 scenarios, in terms of contraction in suitable areas and altitude ranges. Conservation priority should be given to northwestern Yunnan, northeastern Yunnan, and northwestern Guizhou to alleviate the stress of massive habitat loss and extinction. Refugial areas should be established in all four suitable centres to maintain genetic diversity of B. thaidina in China.

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Global alterations in areas of suitability for maize production from climate change and using a mechanistic species distribution model (CLIMEX)

Scientific Reports ◽

10.1038/s41598-017-05804-0 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 37

Author(s):

Nadiezhda Y. Z. Ramirez-Cabral ◽

Lalit Kumar ◽

Farzin Shabani

Keyword(s):

Climate Change ◽

Species Distribution ◽

Species Distribution Model ◽

Distribution Model ◽

Maize Production

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Predicting the Current and Future Suitable Habitat Distribution of Swimming Crab (Portunus trituberculatus) using the Species Distribution Model under Climate Change

10.20944/preprints202101.0189.v1 ◽

2021 ◽

Author(s):

Xinyu Liu ◽

Xiaolu Han ◽

Zhiqiang Han

Keyword(s):

Climate Change ◽

Species Distribution ◽

Species Distribution Model ◽

Seawater Temperature ◽

The Yellow Sea ◽

Distribution Model ◽

Portunus Trituberculatus ◽

Suitable Habitat ◽

Different Seasons ◽

Rcp 8.5

Species have shown their habital variations in responding to climate change, especially during the spring and summer spawning seasons. The species distribution model (SDM) is considered the most favorable tool to study the potential effects of climate change on species distribution. Therefore, we developed the ensemble SDM to predict the changes in species distribution of Portunus trituberculatus among different seasons in 2050 and 2100 under the climate scenarios RCP4.5 and RCP8.5. The results of SDM indicate that the distribution of this species will move northward and have obviouse seasonal variations. Meanwhile, the suitable habitat for the species will be significantly reduced in summer, with loses rates ranging from 45.23% (RCP4.5) to 88.26% (RCP.8.5) by 2100s. Habitat reduction will mainly occur in the East China Sea and southern part of the Yellow Sea, while there will be a small increase in the northern Bohai Sea. These findings will be important to manage the ecosystem and fishery, provide an information forecast of this species in the future, and maintain species diversity if the seawater temperature rises.

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Background sampling and transferability of species distribution model ensembles under climate change

Global and Planetary Change ◽

10.1016/j.gloplacha.2018.03.008 ◽

2018 ◽

Vol 166 ◽

pp. 19-29 ◽

Cited By ~ 5

Author(s):

Maialen Iturbide ◽

Joaquín Bedia ◽

José Manuel Gutiérrez

Keyword(s):

Climate Change ◽

Species Distribution ◽

Species Distribution Model ◽

Distribution Model ◽

Model Ensembles

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Using species distribution model to predict the impact of climate change on the potential distribution of Japanese whiting Sillago japonica

Ecological Indicators ◽

10.1016/j.ecolind.2019.05.023 ◽

2019 ◽

Vol 104 ◽

pp. 333-340 ◽

Cited By ~ 7

Author(s):

Zhixin Zhang ◽

Shengyong Xu ◽

César Capinha ◽

Robbie Weterings ◽

Tianxiang Gao

Keyword(s):

Climate Change ◽

Species Distribution ◽

Potential Distribution ◽

Species Distribution Model ◽

Distribution Model ◽

Impact Of Climate Change ◽

Sillago Japonica ◽

The Impact

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Evaluation of urban road trees in terms of ecosystem services according to climate change scenarios and species distribution model: The case of Robinia pseudoacacia L.

Turkish Journal of Forestry | Türkiye Ormancılık Dergisi ◽

10.18182/tjf.559883 ◽

2019 ◽

pp. 142-148 ◽

Cited By ~ 3

Author(s):

E. Seda Arslan

Keyword(s):

Climate Change ◽

Ecosystem Services ◽

Species Distribution ◽

Species Distribution Model ◽

Robinia Pseudoacacia ◽

Distribution Model ◽

Climate Change Scenarios ◽

Urban Road ◽

Robinia Pseudoacacia L

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Limitations of Species Distribution Models Based on Available Climate Change Data: A Case Study in the Azorean Forest

Forests ◽

10.3390/f10070575 ◽

2019 ◽

Vol 10 (7) ◽

pp. 575 ◽

Cited By ~ 2

Author(s):

Lara Dutra Silva ◽

Eduardo Brito de Azevedo ◽

Francisco Vieira Reis ◽

Rui Bento Elias ◽

Luís Silva

Keyword(s):

Climate Change ◽

Species Distribution ◽

Species Interactions ◽

Species Distribution Model ◽

Selection Process ◽

Additive Models ◽

Machine Learning Techniques ◽

Integrated Modelling ◽

Distribution Model ◽

Careful Study

Climate change is gaining attention as a major threat to biodiversity. It is expected to further expand the risk of plant invasion through ecosystem disturbance. Particularly, island ecosystems are under pressure, and climate change may threaten forest-dependent species. However, scientific and societal unknowns make it difficult to predict how climate change and biological invasions will affect species interactions and ecosystem processes. The purpose of this study was to identify possible limitations when making species distribution model projections based on predicted climate change. We aimed to know if climatic variables alone were good predictors of habitat suitability, ensuring reliable projections. In particular, we compared the performance of generalized linear models, generalized additive models, and a selection of machine learning techniques (BIOMOD 2) when modelling the distribution of forest species in the Azores, according to the climatic changes predicted to 2100. Some limitations seem to exist when modelling the effect of climate change on species distributions, since the best models also included topographic variables, making modelling based on climate alone less reliable, with model fit varying among modelling approaches, and random forest often providing the best results. Our results emphasize the adoption of a careful study design and algorithm selection process. The uncertainties associated with climate change effect on plant communities as a whole, including their indigenous and invasive components, highlight a pressing need for integrated modelling, monitoring, and experimental work to better realize the consequences of climate change, in order to ensure the resilience of forest ecosystems in a changing world.

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