Predicting the potential impacts of climate change on the endangered endemic annonaceae species in east africa

Mkala, Elijah Mbandi; Mwanzia, Virginia; Nzei, John; Oluoch, Wyclife Agumba; Ngarega, Boniface K.; Wanga, Vincent Okello; Oulo, Milicent Akinyi; Mutie, Fedrick Munyao; Kilingo, Flory Mkangombe; Rono, Penninah; Waswa, Emmanuel Nyongesa; Mutinda, Elizabeth Syowai; Ochieng, Clintone Onyango; Mwachala, Geoffrey; Hu, Guang-Wan; Wang, Qing-Feng; Katunge, Jacinta Kaweze; Victoire, Izabayo

doi:10.1016/j.heliyon.2023.e17405

Cited by 2 publications

(1 citation statement)

References 86 publications

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“…Accurate forecasts of species diversity and composition are essential for formulating strategic management policies and conservation measures to prevent biodiversity decline and associated crises (Farooqi et al, 2022). In the past ten years, numerous forecasts have utilized this system to evaluate the influence of climate change on biodiversity across a variety of organisms (Weiskopf et al, 2020;Mkala et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Bioclimatic Modeling of Current Geographic Distribution and Future Range Shifts of Selected Edible Mushrooms in Nigeria

Tiamiyu,

Lateef,

Ajadi

et al. 2024

SAJOLS

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Mushrooms, as part of the fungal kingdom, are essential components in nutrient cycling and carbon retention in terrestrial ecosystems. Monitoring the impact of climate change on fungi in their natural habitat is difficult because most species reside below the soil surface. As a result of few reported occurrence records in Nigeria, we model the species distribution of two edible mushrooms, namely, Pleurotus ostreatus and Macrolepiota procera, using MaxEnt to predict the potential future range shifts under different climate change scenarios. In this study, we have calculated high model performances based on the Area Under Curve (AUC) values generated (0.778-0.873). In the modeling approach, the two species were predicted to have an expansion of their localized fundamental niches, pointing to the influence of precipitation as an important macroclimatic predictor. Highly suitable habitats for the two species were discovered primarily in Southern Nigeria, with less habitat suitability in the Northcentral zone in 2050. The predicted models in this study do not tell missing geographical information, which could be achieved through citizen science for occurrence records and biodiversity conservation. However, they may be used to explore potentialities, such as understanding the possible distribution patterns of the two mushroom species in Nigeria. This can serve as a useful baseline to enhance the utilization and conservation efforts of these macrofungi as a result of climate change, habitat loss, and rapid urbanization.

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Section: Discussionmentioning

confidence: 99%

Bioclimatic Modeling of Current Geographic Distribution and Future Range Shifts of Selected Edible Mushrooms in Nigeria

Tiamiyu,

Lateef,

Ajadi

et al. 2024

SAJOLS

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Evaluation and correction analysis of the regional rainfall simulation by CMIP6 over Sudan

Babiker,

Tan,

Abdallah

et al. 2024

Geographica Pannonica

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This study utilizes satellite-based rainfall CHIRPS to evaluate GCMs-CMIP6 models over Sudan from 1985 to 2014. Overall, the GCMs of BCC-CSM2-MR, CAMS-CSM1-0, CESM2, ECEarth3-Veg, GFDL-ESM4, MIROC-ES2L, and NorESM2-MM are well reproduced in the unimodal pattern of June to September (JJAS), and hence employed to calculate Multi-Model Ensemble (MME). Then, we examine the capability of the GCMs and MME in replicating the precipitation patterns on annual and seasonal scales over Sudan using numerous ranking metrics, including Pearson Correlation Coefficient (CC), Standard Deviation (SD), Taylor Skill Score (TSS), Mean Absolute Error (MAE), absolute bias (BIAS), and, normalized mean root square error (RMSD). The results show that the MME has the lowest bias and slightly overestimates rainfall over most parts of our study domain, whilst, others (ACCESS-CM2, BCC-CSM2-MR, CAMS-CSM1-0, CESM2, CNRM-CM6-1, CNRM-CM6-1-HR, CNRM-ESM2-1, FGOALS-f3-L, FGOALS-g3) consistently overestimate rainfall in referring to CHIRPS data, respectively, but FIO-ESM-2-0 underestimates bias value. Moreover, MIROC-ES2L and NorESM2-MM demonstrate better performance than the other models. Finally, we employed a bias correction (BC) technique, namely Delta BC, to adjust the GCMs model products through the annual and monsoon seasons. The applied bias correction technique revealed remarkable improvement in the GCMs against the observations, with an improvement of 0 - 18% over the original. However, MME and MIROC-ES2L show better performance after correction than other models.

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Predicting the potential impacts of climate change on the endangered endemic annonaceae species in east africa

Cited by 2 publications

References 86 publications

Bioclimatic Modeling of Current Geographic Distribution and Future Range Shifts of Selected Edible Mushrooms in Nigeria

Bioclimatic Modeling of Current Geographic Distribution and Future Range Shifts of Selected Edible Mushrooms in Nigeria

Evaluation and correction analysis of the regional rainfall simulation by CMIP6 over Sudan

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