A three-step method for optimizing abundance monitoring: application to Manila clam surveys in Arcachon Bay (France)
Abstract
Monitoring species abundance requires sampling strategies that are both statistically robust and operationally feasible. This study presents a practical method and an accompanying R workflow that help practitioners identify the most efficient probabilistic sampling design and determine the minimum sample size needed for abundance estimation in finite populations. The method relies on reconstructing abundance on the statistical population through spatial modelling, then simulating alternative sampling designs—spatially balanced or not—to quantify the precision of abundance estimates across increasing sample sizes. Precision is expressed using the margin of error, an intuitive metric directly linked to confidence intervals and decision-making needs. The main novelty of this work lies in three contributions: (i) generalizing the optimization framework to any finite population and to a wide range of probabilistic sampling designs, (ii) providing an automated and reproducible three-step workflow that identifies the smallest sample size achieving a predefined precision threshold through a simulation procedure, and (iii) demonstrating its operational relevance through a real-world implementation. The method was applied to a burrowing invertebrate, the Manila clam Ruditapes philippinarum in Arcachon Bay (France), where historical monitoring relied on stratified simple random sampling (StRS). Using kriging-based spatial interpolation to reconstruct abundance, simulations showed that achieving a 10% margin of error required approximately 290 sites under a spatially balanced design, compared with about 480 under simple random sampling. Field implementation confirmed that the optimized spatially balanced design maintained or improved precision relative to the historical protocol. Nevertheless, comparisons with the historical protocol should be interpreted cautiously because surveys were conducted in different years under potentially different ecological conditions. Overall, the framework provides a transparent, adaptable, and cost-effective tool for optimizing abundance monitoring programs.
Related
- Separating Faces in ARMS Metabarcoding Improves Marine Biodiversity Monitoring: A Comparison Across Protocols, Experimental Designs and Photographic Surveys
- Optimizing induced seismicity monitoring for CO2 injection at Trüllikon, Switzerland using risk-informed guidelines
- Integrating high-resolution magnetic surveys and metaheuristic optimization for quantitative characterization of subsurface magnetic sources in a municipal landfill
- When n is not enough: Assessing the interplay of spatial sampling error and sample selection in abundance estimation
- Every drop counts: optimizing water use efficiency for sustainable tomato production
BibTeX
@article{kermorvant2026threestep,
title = {{A three-step method for optimizing abundance monitoring: application to Manila clam surveys in Arcachon Bay (France)}},
author = {Claire Kermorvant and Noëlle Bru and Frank d'Amico and Florence Sanchez and Muriel Lissardy and Nathalie Caill-Milly},
journal = {Aquatic Living Resources},
year = {2026},
month = sep,
doi = {10.1051/alr/2026028},
url = {https://univ-pau.hal.science/hal-05786670v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - A three-step method for optimizing abundance monitoring: application to Manila clam surveys in Arcachon Bay (France)
AU - Claire Kermorvant
AU - Noëlle Bru
AU - Frank d'Amico
AU - Florence Sanchez
AU - Muriel Lissardy
AU - Nathalie Caill-Milly
PY - 2026
DA - 2026/09/22
JO - Aquatic Living Resources
DO - 10.1051/alr/2026028
UR - https://univ-pau.hal.science/hal-05786670v1
AB - Monitoring species abundance requires sampling strategies that are both statistically robust and operationally feasible. This study presents a practical method and an accompanying R workflow that help practitioners identify the most efficient probabilistic sampling design and determine the minimum sample size needed for abundance estimation in finite populations. The method relies on reconstructing abundance on the statistical population through spatial modelling, then simulating alternative sampling designs—spatially balanced or not—to quantify the precision of abundance estimates across increasing sample sizes. Precision is expressed using the margin of error, an intuitive metric directly linked to confidence intervals and decision-making needs. The main novelty of this work lies in three contributions: (i) generalizing the optimization framework to any finite population and to a wide range of probabilistic sampling designs, (ii) providing an automated and reproducible three-step workflow that identifies the smallest sample size achieving a predefined precision threshold through a simulation procedure, and (iii) demonstrating its operational relevance through a real-world implementation. The method was applied to a burrowing invertebrate, the Manila clam Ruditapes philippinarum in Arcachon Bay (France), where historical monitoring relied on stratified simple random sampling (StRS). Using kriging-based spatial interpolation to reconstruct abundance, simulations showed that achieving a 10% margin of error required approximately 290 sites under a spatially balanced design, compared with about 480 under simple random sampling. Field implementation confirmed that the optimized spatially balanced design maintained or improved precision relative to the historical protocol. Nevertheless, comparisons with the historical protocol should be interpreted cautiously because surveys were conducted in different years under potentially different ecological conditions. Overall, the framework provides a transparent, adaptable, and cost-effective tool for optimizing abundance monitoring programs.
SN - 0990-7440
SN - 1765-2952
ER -