Coral reef islands are known to exhibit significant sub- and inter-annual coastal variability. Maldivian coastlines are not systematically or frequently monitored, so modes of variability and morphological responses to climate forcings are not well understood and characterized. Here, a data processing pipeline was developed to reveal spatiotemporal patterns of coastal change by leveraging medium-resolution satellite imagery. The pipeline was applied to the islands of Huvadhoo Atoll, Maldives, but is also applicable to other Small Island Developing States. Coastline positions were extracted monthly from Sentinel-2, Landsat-8 and 9 images, using an image segmentation algorithm. Trend and seasonal signals were disentangled from time series of coastline position, to reveal short- and long-term modes of variability. Results show island- and atoll-scale spatial patterns of coastal change. 28% of the atoll’s studied islands show detectable oscillating coastline position changes, suggesting sub-annual alongshore sediment transport, coinciding with the phases of the Indian Monsoon and regional changes in met-ocean conditions. Islands located in the lagoon experience greater variability than those on the atoll rim. Seasonal erosion and accretion periods were found to be asymmetrical and aligned with the duration of the Indian Monsoon phases. The seasonal signal is modulated by inter-annual variability in met-ocean conditions (e.g., Indian Ocean Dipole events). Therefore, in order to forecast the impact of climate change on coral reef island equilibrium, drivers likely to compound sea level rise need to be identified and their effects quantified. These results can be used to support evidence-based environmental policies, coastal planning, and marine infrastructure development.
Understanding how anthropogenic environmental signals reorganize over time is central to Environmental Data Science, particularly because long-run emission trajectories reflect interacting technological, institutional, behavioral, and macroeconomic processes whose effects need not appear as smooth changes in level. This study develops a reproducible analytical framework that combines rolling spectral entropy with Bayesian change-point inference to examine structural transitions in annual transport-related CO2 emissions across the G7 economies from 1970 to 2023. The analysis adopts a structure-aware interpretation of the series while specifying what spectral entropy can and cannot establish in short annual windows. Entropy is interpreted as the concentration or dispersion of detrended within-window variability across resolvable Fourier components, rather than as a direct physical measure of resilience. The baseline window is W = 10 $ W=10 $ upper W equals 10 years, with W = 8 $ W=8 $ upper W equals 8 and W = 12 $ W=12 $ upper W equals 12 used for sensitivity assessment. Country-specific Bayesian single-mean-shift models are estimated independently, and posterior means are reported together with 90% credible intervals and the shape of the complete posterior distribution. The results reveal recurrent entropy troughs and collapse-rate spikes in the early 1980s, around 1998–2000, and in the late 2000s. The late-1990s episode constitutes a conspicuous descriptive cross-country alignment, but the Bayesian evidence is heterogeneous: several countries exhibit broad or multimodal posteriors, so the analysis does not treat a common G7 change year as established. The recent-collapse calculation is interpreted in light of the exceptional COVID-19 shock. Overall, the evidence supports the value of spectral descriptors as exploratory indicators of changing temporal organization, provided that short-window frequency resolution, autocorrelation, transient shocks, and posterior uncertainty are made explicit.
This article contributes a machine learning approach to detecting environmental crimes in Brazil. Detecting these crimes is important for the health of the environment and public safety. The measurement of environmental crimes is challenging, in part due to efforts of those involved to remain hidden. However, administrative data on embargoes—legal liens on parcels of land—identify locations where the government documented some of these crimes. Using these data, the analysis develops a method for detecting yet-unidentified locations of these crimes. Embargo data are merged with a wide range of predictors that capture features of the physical environment, as well as demographic, socioeconomic, political, and public safety attributes. After training a series of models on these data, the random forest (RF) model performs best (AUC = 0.92). Overall, a combination of changes in vegetation, homicide rates, the prevalence of young men, human development, employment, the presence of a poverty reduction program, and political factors that capture local electoral competition and participation are important predictors of illegal deforestation. These findings—some unsurprising and some surprising—show the value of multicausal, cross-disciplinary approaches to understanding and detecting environmental crime. The practical gains from integrating remote sensing, a wide range of administrative data, and machine learning help build more effective crime detection, enhancing the government’s ability to conduct forest enforcement more safely and efficiently. Future research is promising as more and better data become available, and the approach can be extended to detect other illicit behavior, in Brazil and elsewhere.
Artificial intelligence (AI) can rapidly process large ecological datasets, uncover patterns, and inform conservation decisions, but responsible adoption depends as much on human-centric skills as on technical methods. Ecologists face steep learning curves, an overwhelming and fast-evolving model landscape, uneven access to data and computing, and a growing transparency deficit. These challenges require human-centric skills like time management, critical thinking, collaboration, communication, creativity, and project management to select, implement, interpret, and responsibly translate AI outputs into ecological insight and action. We led a workshop, EcoViz+AI: Visualization and AI for Ecology, that brought together 35 experts to synthesize practical guidance for navigating these challenges across the AI pipeline. Using workshop discussions and experiences as a foundation, this position paper proposes practical solutions and complementary human-centric skill development to address these challenges: (1) educational resources that support opportunity-cost reasoning (time management) and methodological judgment (critical thinking), (2) communities of practice that build inclusive shared expertise (collaboration and mentorship), (3) effective visualizations that improve interpretability and strengthen transparency of model behavior and uncertainty (creativity and communication), and (4) computational resources that reduce implementation burden through shared data, extensible code, and accessible infrastructure (project management and problem-solving). Our workshop compiled resources, including science communication videos for five AI use cases and repositories for ecology-related AI models and communities of practice. Emphasizing human-centric skills and working in tandem with efforts to promote open science and computational literacy can make AI in ecology more rigorous, equitable, and ecologically relevant, advancing research and conservation.
Multi-model ensembles are widely used in climate science, yet Coupled Model Intercomparison Project Phase 6 (CMIP6) models are neither independent nor equally skillful. We present a framework for interpretable ensemble learning that improves prediction while making model contributions explicit. Using monthly CMIP6 near-surface temperature and precipitation (1948–2014) against ERA5, we compare arithmetic multi-model ensemble (AMME) averaging with Ridge regression, random forest, and a continuous gating mixture that adaptively interpolates between them. On held-out test years (2006–2014), gating achieves the best temperature performance and improves precipitation distributional behavior relative to both AMME and individual learners. We trace these gains back to individual models and space: dominant-cluster and dominant-model maps show that gating reallocates trust in regionally coherent patterns rather than collapsing to a single best model, while entropy-based diagnostics quantify where trust is concentrated versus distributed. These analyses provide a practical pathway for transparent ensemble learning that supports both predictive skill and scientific accountability.
