Projections for Mexico’s Tropical Rainforests Considering Ecological Niche and Climate Change

Santos-Hernández, Antonio Fidel; Monterroso-Rivas, Alejandro Ismael; Granados-Sánchez, Diódoro; Villanueva‐Morales, Antonio; Santacruz-Carrillo, Malinali

doi:10.3390/f12020119

Cited by 16 publications

(9 citation statements)

References 47 publications

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“…Spatial pattern changes refer to those of potential suitable regions of species across different periods, which could be obtained by superposing binary prediction maps of suitable regions during different periods (Santos‐Hernández et al, 2021; Wu et al, 2021; Zurell et al, 2020). For this study, we created a prediction chart of the spatial pattern changes of four different SSPs (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5) during current and future periods (2050s, 2070s, and 2090s), resulting in a total of 12 pattern change predictions.…”

Section: Methodsmentioning

confidence: 99%

“…The set of 19 bioclimatic variables used in the present study were downloaded from the WorldClim website (https://www.worldclim.org), which involved a recent (1970–2000) and three future periods (2050s, 2070s, and 2090s). Considering the impacts of climate change scenarios on the accuracy of model development (Santos‐Hernández et al, 2021), we selected four shared socioeconomic pathways (SSPs; SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5) for three general circulation models (GCMs; BCC‐CSM2‐MR, CNRM‐CM6‐1, and MIROC‐ES2L) in future climate data (Xu et al, 2020). Consequently, a total of 37 sets of bioclimatic data were included in this study, with one recent, and 36 future sets.…”

Section: Methodsmentioning

confidence: 99%

“…Climate change is considered to be a key factor in altering the geographical distribution of species in the 21st century (Record et al, 2013; Santos‐Hernández et al, 2021; Waltari et al, 2007). According to the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), the average global surface temperature is expected to rise by 0.3–4.8°C by the end of the 21st century due to the continuous increases in greenhouse gas emissions (Braunisch et al, 2013; Cahill et al, 2013; Carroll et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Predicting potential distribution of Ziziphus spinosa (Bunge) H.H. Hu ex F.H. Chen in China under climate change scenarios

Zhao

Lü

et al. 2022

Ecology and Evolution

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Ziziphus spinosa (Bunge) H.H. Hu ex F.H. Chen is a woody plant species of the family Rhamnaceae (order Rhamnales) that possesses high nutritional and medicinal value. Predicting the effects of climate change on the distribution of Z. spinosa is of great significance for the investigation, protection, and exploitation of this germplasm resource. For this study, optimized maximum entropy models were employed to predict the distribution patterns and changes of its present (1970–2000) and future (2050s, 2070s, and 2090s) potential suitable regions in China under multiple climate scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 & SSP5‐8.5). The results revealed that the total area of the present potential suitable region for Z. spinosa is 162.60 × 104 km2, which accounts for 16.94% of China's territory. Within this area, the regions having low, medium, and high suitability were 80.14 × 104 km2, 81.50 × 104 km2, and 0.96 × 104 km2, respectively, with the high suitability regions being distributed primarily in Shanxi, Hebei, and Beijing Provinces. Except for SSP‐1‐2.6‐2070s, SSP‐5‐8.5‐2070s, and SSP‐5‐8.5‐2090s, the suitable areas for Z. spinosa in the future increased to different degrees. Meanwhile, considering the distribution of Z. spinosa during different periods and under different climate scenarios, our study predicted that the low impact areas of Z. spinosa were mainly restricted to Shanxi, Shaanxi, Ningxia, Gansu, Liaoning, Inner Mongolia, and Jilin Provinces. The results of core distributional shifts showed that, except for SSP1‐2.6, the center of the potential suitable region of Z. spinosa exhibited a trend of gradually shifting to the northwest.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting potential distribution of Ziziphus spinosa (Bunge) H.H. Hu ex F.H. Chen in China under climate change scenarios

Zhao

Lü

et al. 2022

Ecology and Evolution

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“…Species in ecological communities reflect the interactions among organisms and between organisms and their abiotic environments (Cardinaux et al, 2018 ; Koffel et al, 2021 ; Walther et al, 2002 ). Many researchers have focused on the response of communities to global changes, and an in‐depth understanding of species interactions can help to predict their responses to climate change (Enquist, 2002 ; Gilman et al, 2010 ; Ovaskainen et al, 2013 ; Santos‐Hernández et al, 2021 ). Climate change can lead to inconsistencies in the phenology of species, which in turn leads to community changes (Ovaskainen et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting the distribution of plant associations under climate change: A case study on Larix gmelinii in China

Chen

Zhang

Wan

et al. 2022

Ecology and Evolution

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Association is the basic unit of plant community classification. Exploring the distribution of plant associations can help improve our understanding of biodiversity conservation. Different associations depend on different habitats and studying the association level is important for ecological restoration, regional ecological protection, regulating the ecological balance, and maintaining biodiversity. However, previous studies have only focused on suitable distribution areas for species and not on the distribution of plant associations. Larix gmelinii is a sensitive and abundant species that occurs along the southern margin of the Eurasian boreal forests, and its distribution is closely related to permafrost. In this study, 420 original plots of L. gmelinii forests were investigated. We used a Maxent model and the ArcGIS software to project the potential geographical distribution of L. gmelinii associations in the future (by 2050 and 2070) according to the climate scenarios RCP 2.6, RCP 4.5, and RCP 8.5. We used the multi‐classification logistic regression analysis method to obtain the response of the suitable area change for the L. gmelinii alliance and associations to climate change under different climate scenarios. Results revealed that temperature is the most crucial factor affecting the distribution of L. gmelinii forests and most of its associations under different climate scenarios. Suitable areas for each association type are shrinking by varying degrees, especially due to habitat loss at high altitudes in special terrains. Different L. gmelinii associations should have different management measures based on the site conditions, composition structure, growth, development, and renewal succession trends. Subsequent research should consider data on biological factors to obtain more accurate prediction results.

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“…For many users, MAXENT is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts [16]. Due to its advantages, the MAXENT model has been widely applied to predict and evaluate the potential distribution of different species under climate change scenarios [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Modeling the Potential Distribution of Three Taxa of Akebia Decne. under Climate Change Scenarios in China

Xiuting

Zhang

Zhao

et al. 2021

Forests

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Akebia trifoliata (Thunb.) Koidz., Akebia trifoliata subsp. australis (Diels) T. Shimizu and Akebia quinata (Houtt.) Decne. are the source plants of the traditional Chinese medicines AKEBIAE CAULIS and AKEBIAE FRUCTUS, and have high pharmaceutical value. However, the resource reserve of these plants has dramatically declined due to habitat destruction, which has seriously affected their adequate supply and sustainable utilization. A poor knowledge of the potential distribution of these medicinal materials would seriously constrain the protective exploitation of wild resources and the establishment of new cultivations. In this study, based on the scenarios of SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, the maximum entropy model was used to predict the potential distribution of these three Akebia taxa under current and future (2030s, 2050s, 2070s and 2090s) climate conditions. Our findings showed that the potentially suitable areas of these three Akebia taxa were mainly distributed in China at 101.8–121.9° E and 23.5–34.6° N. Temperature played a more significant role than precipitation in affecting the distribution. The dominant bioclimatic variable that affected the distribution of A. trifoliata and A. quinata in China was the minimum temperature of the coldest month (BIO06). For A. trifoliata subsp. australis, the mean diurnal range (BIO02) was the dominant variable influencing its distribution. Compared with current conditions, the moderate- and high-suitability areas of these three Akebia taxa were predicted to shrink towards the core areas, while the low-suitability areas were all observed to increase from the 2030s to the 2090s. With the increase in radiative forcing of SSP, the low-impact areas of these three Akebia taxa showed a decreasing trend as a whole. Our results illustrate the impact of climate change on the distribution of Akebia, and would provide references for the sustainable utilization of Akebia’s resources.

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Projections for Mexico’s Tropical Rainforests Considering Ecological Niche and Climate Change

Cited by 16 publications

References 47 publications

Predicting potential distribution of Ziziphus spinosa (Bunge) H.H. Hu ex F.H. Chen in China under climate change scenarios

Predicting potential distribution of Ziziphus spinosa (Bunge) H.H. Hu ex F.H. Chen in China under climate change scenarios

Predicting the distribution of plant associations under climate change: A case study on Larix gmelinii in China

Modeling the Potential Distribution of Three Taxa of Akebia Decne. under Climate Change Scenarios in China

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