Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo

Simulation and optimization of land use pattern to embed ecological suitability in an oasis region: A case study of Ganzhou district, Gansu province, China

High-intensity land use has led to water resource imbalance and land degradation in oasis regions, which pose a great threat to ecological security. Optimization of land use patterns is crucial to ensuring the rational distribution of water and land resources and improving the stability of oasis ecosystems. The purpose of this study is to spatially allocate land use activities to more suitable regions. In this study, we first evaluated the land ecological suitability (LES) in Ganzhou District, a typical oasis region. Then, the LES evaluation results were embedded in an integrated CA-Markov model based on multiple criteria evaluation (MCE) and multi-objective land allocation (MOLA) to simulate and optimize land use patterns for the year 2025 under two scenarios, i.e., Business as Usual (BAU) and Land Ecological Optimization (LEO). The results revealed that the optimized land use pattern generated by LES was more reasonable. The growth rate of construction land was restricted, and a slightly increased area of construction land mainly occupied unused land. Farmland area had a decreasing trend, and was mainly converted to grassland. Moreover, the woodland and water areas had increasing trends. This study can serve as a scientific reference for planners and policy makers in formulating land use planning and land use resource management strategies in oasis regions.

Modeling land cover change dynamic using a hybrid model approach in Qeshm Island, Southern Iran

Multiple factors including natural and human-induced ones lead to land cover change in the landscape. Therefore, identifying the pattern of land cover change can help inform land-use management and prevent associated issues which can affect the natural resources of the landscape. The aim of this study is to assess land cover change in the Qeshm Island in southern Iran by combining the resulting outputs of multiple modeling methods, cellular automata (CA), Markov chains, and artificial neural networks (ANN) based on land cover maps for the years 1996, 2006, and 2016 that have been extracted from satellite imagery (Landsat 5, 7, and 8). In order to evaluate the accuracy of modeling, the Kappa coefficient was calculated to be 0.8. Then, land cover changes for 2025 were predicted by a hybrid model (CA-Markov-ANN). The results indicate that the classes of built-up areas, vegetation, and mangrove forests have changed more significantly from 1996 to 2016 compared with other classes. Land cover maps generated in this study showed that built-up areas have grown significantly in recent decades due to the region's growing population and development of ports, commercial, and industrial areas. Due to the climate change, the land area covering vegetation has decreased dramatically. The size of the mangrove forests has increased over the time period of the study (1996-2025). The findings of this study can inform land-use planning decisions by providing them with a comprehensive overview of land cover conditions in the future.

Dynamic projection of ecological risk in the Manas River basin based on terrain gradients

With large-scale developments, the Manas River Basin (MRB) is in an extreme imbalance especially in land use, thus causing a series of ecological problems. A reliable dynamic ecological risk assessment is expected to provide useful information for the economic development. Through coupling spatial Cellular Automaton-Markov (CA-Markov) model and Landsat satellite images in 2000, 2008 and 2016, we forecasted the land use maps in 2024 and 2032. Based on the ecological risk model, we evaluated the ecological risk at landscape level from 2000 to 2032. More importantly, an improved evaluation of ecological risk was proposed based on terrain gradients and the correlation between terrain niche index (TNI) and future ecological risk was analyzed. The results showed that the artificial oases and urban are expanding, while the natural grassland is shrinking. Corresponding to the rapid development stage and stable consolidation stage, farmland will be followed by a slower increase (2016-2032) after a rapid increase (2000-2016), and water decreases first but then is projected to recover. As the overall spatial diversity increasing, the ecological risk in the whole basin is growing, especially in grassland. Compared with the stable critical state in artificial landscape, the future ecological risks in natural landscape tend to increase due to the cumulative effects of human activities. Also, we found that the great ecological risk mainly happens in "high altitude and complex terrain" or "low altitude and flat terrain" areas. The future ecological risk in medium terrain niche index (TNI) gradient will increase, while it will decrease in the lowest. Above all, the proposed framework can do well in forecasting ecological risk at landscape level, and can help simply infer the changes of ecological risk based on terrain.

Among-site variability in the stochastic dynamics of East African coral reefs

Coral reefs are dynamic systems whose composition is highly influenced by unpredictable biotic and abiotic factors. Understanding the spatial scale at which long-term predictions of reef composition can be made will be crucial for guiding conservation efforts. Using a 22-year time series of benthic composition data from 20 reefs on the Kenyan and Tanzanian coast, we developed Bayesian vector autoregressive state-space models for reef dynamics, incorporating among-site variability, and quantified their long-term behaviour. We estimated that if there were no among-site variability, the total long-term variability would be approximately one-third of its current value. Thus, our results showed that among-site variability contributes more to long-term variability in reef composition than does temporal variability. Individual sites were more predictable than previously thought, and predictions based on current snapshots are informative about long-term properties. Our approach allowed us to identify a subset of possible climate refugia sites with high conservation value, where the long-term probability of coral cover ≤0.1 (as a proportion of benthic cover of hard substrate) was very low. Analytical results show that this probability is most strongly influenced by among-site variability and by interactions among benthic components within sites. These findings suggest that conservation initiatives might be successful at the site scale as well as the regional scale.