Climate change threatens seagrass ecosystems, which are vital for blue carbon sequestration and associated co-benefits. Our study on Hainan Island in the South China Sea assessed climate impacts on seagrass habitats using ensemble modeling under two scenarios (RCP4.5 and RCP8.5) for 2050 and 2100. We found the current seagrass carbon stock is approximately 1.194 Tg, but projections show habitat suitability could decrease by up to 74.78% by 2100 under the severe scenario, potentially causing economic losses of $1.02–1.27 billion. While seagrass restoration requires initial investment, it offers substantial long-term climate benefits. Our analysis indicates seagrasses may migrate toward higher latitudes due to climate change, though new habitats could emerge along Hainan’s southern coast by 2100. These findings emphasize the urgency of conservation and restoration efforts to safeguard seagrass ecosystems and their vital role in climate change mitigation strategies.

Global forest area has declined over the past few years, forest quality has declined, and ecological and environmental events have increased with climate change and human activity. In the context of ecological civilization, forest health issues have received unprecedented attention. By improving forest health, forests can better perform their ecosystem service functions and promote green development. This study was carried out in the WuZhi Shan area of Hainan Tropical Rainforest National Park. We employed a decision tree algorithm, a machine learning technique, for our modeling due to its high accuracy and interpretability. The objective weighted method using criteria of importance through intercriteria correlation (CRITIC) was used to determine forest health classes based on survey and experimental data from 132 forest samples. The results showed that species diversity is the most important metric to measure forest health. An interpretable decision tree machine learning model was proposed to incorporate forest health indicators, providing up to 90% accuracy in the classification of forest health conditions. The model demonstrated a high degree of effectiveness, achieving an average precision of 90%, a recall of 67%, and an F1 score of 70.2% in predicting forest health. The interpretable decision tree classification results showed that breast height diameter is the most important variable in classifying the health status of both primary and secondary forests. This study highlights the importance of using interpretable machine learning methods for the decision‐making process. Our work contributes to the scientific underpinnings of sustainable forest development and effective conservation planning.

Globally, seagrass meadows provide critical ecosystem services. However, seagrasses are globally degraded at an accelerated rate. The lack of information on seagrass spatial distribution and seagrass health status seriously hinders seagrass conservation and management. Therefore, this study proposes to combine remote sensing big data with a new hybrid machine learning model (RF-SWOA) to predict potential seagrass habitats. The multivariate remote sensing data is used to train the machine learning model, which can improve the prediction accuracy of the model. This study shows that a hybrid machine learning model (RF-SWOA) can predict potential seagrass habitats more accurately and effectively than traditional models. At the same time, it has been shown that the most important factors influencing the potential habitat of seagrass in the Hainan region were the distance from land (38.2%) and the depth of the ocean (25.9%). This paper provides a more accurate machine learning model approach for predicting the distribution of marine species, which can help develop seagrass conservation strategies to restore healthy seagrass ecosystems.

The biodiversity of temperate forests is severely threatened by climate change, with many species facing loss of suitable habitats or local extinction. Species that are highly sensitive to climate change, especially those with low dispersal ability, pose significant challenges. A comprehensive overview of the impact of climate change on the elevational shifts of temperate trees is still missing. In this study, we used species distribution models to study range shifts of Cormus domestica (L.) in Italy under current and future scenarios (SSP2‐4.5 and SSP5‐8.5), specifically focusing on the species' elevational shifts under future climate change. Our models project a considerable loss of habitat suitability over time (32%–68% by 2100). Annual mean temperature is the primary limiting variable, with the margin of the optimal temperature range at about 12°C. We found that climate change will alter the spatial configuration of suitable areas, with newly gained habitats always at higher elevations than lost ones. This result implies a trend of suitable areas shifting toward higher elevations, but in a way that cannot compensate for habitat loss. Our results point to the urgent need to strengthen conservation efforts aimed at improving the climate resilience of low‐elevation species.