bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.08.27.609936

Analysing drivers of worldwide tidal wetland change

Perrodin, L.·Otero, A. N.·Toor, M.·Canto, R.·Becker, M.·Dong, Y.·Worthington, T. A.·Murray, N. J.
View original source ↗Export metadata ↓

Abstract

Tidal wetlands are dynamic coastal ecosystems that can change in extent in response to a broad range of change drivers. We use high spatial resolution satellite imagery to estimate the relative influence of 18 classes of change drivers on observed tidal wetland gains and losses from 1999 to 2019, differentiating direct drivers as those observable at the site of ecosystem change, and indirect drivers as broader processes that influence changes without being directly visible. We developed a random sample of 2823 change detections from a global dataset of tidal wetland change and allocated each change event to driver classes using high-resolution time-series imagery. We identified that indirect drivers were the most widespread type of driver of tidal wetland change (70.9%), with flooding being the predominant driver for losses (47.5%) and unknown natural processes of change for gains (62.7%). Drivers often associated with climate change were evident in interpretations of wetland drivers, with increases in flooded area and reductions in vegetation cover suggesting the effects of relative sea level rise on tidal wetlands are observable in many areas. Our temporal analysis revealed that over 20 years, indirect drivers consistently contributed to larger proportions of gains and losses compared to direct drivers. Asia was the only continent where direct drivers of loss, such as agriculture (22.9%) and aquaculture (17.1%), outweighed indirect drivers, providing further evidence of the widespread transformation of Asias natural coastal ecosystems to anthropogenic shorelines. Globally, coastal land reclamations were mostly observed in mangrove ecosystems, where more than half of the observed losses were of anthropogenic origin. The most observed direct drivers of gains were altered land management and restoration, but none of them contributed to more than 5% of the total gains over 20 years. Our findings suggest a need for efficient conservation measures that allow the dynamic processes that characterise coastal ecosystems to persist, while simultaneously reducing the worldwide impact of direct human activities.

Explore related subjects

ecologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Perrodin, L., Otero, A. N., Toor, M., Canto, R., Becker, M., Dong, Y., Worthington, T. A., Murray, N. J.. 2024-08-27. Analysing drivers of worldwide tidal wetland change. https://doi.org/10.1101/2024.08.27.609936

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

PlanktonLake-CEREEP- A Freshwater Plankton Image Dataset with Semi-Automated Label Cleaning

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

ecology↗
bioRxiv2026

A training protocol for human classification of Asian elephant images from trail cameras

Trail cameras have become ubiquitous tools for ecological data collection over recent decades. Despite progress in the development of automated algorithms and artificial intelligence for image classification, our ability to process large volumes of data remain limited by the need for trained human observers to make refined judgements. We provide guidance on placement of trail cameras for observing Asian elephants (Elephas maximus) and outline a protocol for training and testing naive human observers in performing image classifications (age/sex class and group composition) that cannot yet be automated. This process can be used to develop a high-throughput workflow capable of extracting useful data from large volumes of images. Our training material consisted of 14,007 images collected from 6 trail cameras around Udawalawe National Park in Sri Lanka from 2017-2019. In the first stage, expert observers (n=3) trained a group of inexperienced participants (n=4), who engaged in an iterative process to develop a protocol document. The document was then tested on a second set of subjects (n=6) each of whom classified 350 test images in four separate sequential batches using quantitative measures of precision and accuracy. The test set was sampled from 54,435 images from an additional 25 cameras. When compared to expert observers, they achieved a fair level of precision (Fleiss' kappa = 0.247) and 82.6% accuracy. Our approach can usefully be extended to other species and contexts.

ecology↗
bioRxiv2026

Forest belowground productivity and carbon allocation predominantly driven by soil properties rather than climate

Forests are threatened by a multitude of stressors, including anthropogenic disturbances and climate change. Assessing how forests will respond to these stressors requires a comprehensive understanding of net primary productivity (Npp), environmental constraints on growth, and adaptive capacity. A parameter of significant uncertainty is belowground Npp (bNpp), which can account for up to 80% of total Npp but is poorly estimated and rarely measured directly. We used a cross-biome dataset of direct, field-based measurements of aboveground and belowground primary productivity and 21 climatic and soil variables to identify potential constraints on bNpp and belowground carbon allocation in boreal and cold temperate forests. Soil variables, rather than climate variables, were the main drivers of bNpp and belowground allocation across biomes. The importance of soil variables suggests that soil nutrient dynamics, especially soil nutrient pool and flux variables, must be explicitly modeled to more accurately predict feedbacks between climate, productivity, and within-tree carbon allocation. Within biomes, environmental drivers of belowground allocation varied between low versus high allocation forests, indicating that environmental drivers are site-specific and the development of within-biome, site-scale classifications for forest ecosystems could be useful. Changes in soil variables, such as increasing soil nitrogen pools, caused abrupt and large decreases in bNpp for boreal, but not cold temperate forests. Threshold-like shifts indicate that boreal forests might have lower adaptive capacity and higher sensitivity to disturbances than cold temperate forests. With 70% of boreal forests characterized by low bNpp, disturbances such as anthropogenic nitrogen deposition could cause large-scale decreases in bNpp that could push these forests beyond their adaptive capacity.

ecology↗