Biology subjects

Ah-Peng, C.

Publications and source records attributed to Ah-Peng, C..

3 recordsLinked to original sources

bioRxiv2026

Global failure to track international biodiversity targets on islands

Islands harbour exceptional biodiversity but face severe threats, making their conservation crucial to achieving the goals and targets of the Kunming-Montreal Global Biodiversity Framework. We therefore evaluated whether existing indicators can track progress toward the Framework's 23 targets across islands worldwide. We screened 504 biodiversity and socioeconomic indicators from the official framework and other global initiatives, retaining 47 indicators. Historical time series from 1950 onward were modelled and projected to 2030. Indicators were also classified using the Driver-Pressure-State-Impact-Response (DPSIR) and Theory of Change (ToC). Results showed uneven progress: eight targets improved across all available indicators, while ten displayed conflicting trends. Nearly half of the indicators focused on policy and management responses showed improvement, whereas trends in biodiversity pressures and states were mixed. Critically, 15 of 23 targets (65%) lacked a suitable headline indicator, and five targets had no indicator at all. Notably, Targets 2 and 8, addressing ecosystem restoration and climate change impacts, respectively, lacked island indicators entirely. Over half of the available indicators (29 of 47; 62%) were reported at the national level, limiting their applicability to subnational island jurisdictions and potentially obscuring differences between mainland and island conditions. Most targets also lacked quantitative objectives, limiting evaluation of whether the rate of change is sufficient to meet the targets by 2030. We recommend island-level reporting, island-specific indicators, and quantitative benchmarks tailored to island needs.

ecology↗

bioRxiv2026

Towards Scaling-Up Three-Dimensional Habitat Structural Measurements with Multi-Sensor Remote Sensing

Terrestrial Laser Scanning (TLS) captures fine-scaled three-dimensional measurements of ecosystem structure, supporting monitoring of the Essential Biodiversity Variables (EBVs). Yet employing TLS across landscapes remains challenging in remote and topographically complex areas. Remote sensing provides a potential pathway for upscaling TLS-derived structural metrics, but to what extent is unquantified particularly in heterogenous environments, like oceanic islands. Here, we investigated the ability of remote sensing to estimate TLS-derived habitat structure across three contrasting habitats (lowland rainforest, montane cloud forest, and subalpine summit scrub) on La Reunion island. Sentinel-1, Sentinel-2, and Aerial LiDAR (ALS) data were acquired over plots where TLS was completed. We derived defined indices of backscatter coefficients, vegetation indices, and LiDAR metrics and assessed their alignment with TLS measurements using a Procrustes analysis. Subsequently, we used General Additive Models to estimate TLS habitat structure from remote sensing variables. Sentinel-2 exhibited the highest multivariate alignment with TLS (r = 0.51). TLS measurements of horizontal and vertical structure were estimated with the highest cross-validated predictive accuracy (R2 0.39 - 0.73), whilst structural complexity metrics were estimated with greater difficulty (R2 0.02 - 0.20). Multi-sensor models outperformed all single-sensor models in prediction estimates. Model performance also varied across habitats, with the highest agreement between predicted and observed values in the lowland rainforest (r = 0.38), and the lowest agreement (r = 0.35) in the montane cloud forest. Yet the dominant structural feature of each habitat was most accurately captured with remote sensing. Our results demonstrate the potential of integrating multi-sensor remote sensing data to upscale key dimensions of TLS-derived ecosystem structure but remains challenging for fine-scale structural complexity. These findings highlight both the potential and constraints of remote sensing for developing scalable, long-term monitoring frameworks for EBVs, especially in structurally complex and underrepresented island ecosystems.

ecology↗

bioRxiv2025

Distribution of carbon monoxide-oxidizing microorganisms along a chronosequence on Piton De La Fournaise volcano

Volcanic soils provide a unique environment for studying microbial colonization and succession due to their extreme conditions and distinct geochemical profiles. This study focused on carbon monoxide (CO)-oxidizing microbial communities in volcanic soils of varying ages at Piton De La Fournaise (Reunion island). Soil samples from three sites were analyzed to assess microbial community structure using 16S rRNA gene sequencing and metagenomic analysis to identify functional genes involved in CO oxidation. The activity of CO oxidizing microbes in soils was measured. Phylum-level analysis showed increasing Acidobacteriota and Chloroflexota, decreasing Actinomycetota and Bacteroidota, and stable Pseudomonadota, while class-level patterns included rising Alphaproteobacteria and Acidobacteriia, with Ktenobacteria emerging in the oldest soils. CO dehydrogenase-related genes were found in 17 metagenome-assembled genomes across all sites. CO-oxidizing microbes were present across soil ages, with detectable activity in the younger soils and greatest activity in the oldest, suggesting that these microbes actively use CO as an energy source even in soils with primary vegetation, contrary to general understanding. The findings highlight the intricate dynamics of microbial succession in volcanic soils and challenge conventional expectations about community complexity over time. Understanding pioneer communities elucidates soil restoration processes, which will become critical when countering anthropogenic soil degradation.

microbiology↗