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Optimizing speleological monitoring efforts: insights from long-term data for tropical iron caves.

ABSTRACT: Understanding the factors underpinning species abundance patterns in space and time is essential to implement effective cave conservation actions. Yet, the methods employed to monitor cave biodiversity still lack standardization, and no quantitative assessment has yet tried to optimize the amount and type of information required to efficiently identify disturbances in cave ecosystems. Using a comprehensive monitoring dataset for tropical iron caves, comprising abundance measurements for 33 target taxa surveyed across 95 caves along four years, here we provide the first evidence-based recommendations to optimize monitoring programs seeking to follow target species abundance through time. We found that seasonality did not influence the ability to detect temporal abundance trends. However, in most species, abundance estimates assessed during the dry season resulted in a more accurate detection of temporal abundance trends, and at least three surveys were required to identify global temporal abundance trends. Finally, we identified a subset of species that could potentially serve as short-term disturbance indicators. Results suggest that iron cave monitoring programs implemented in our study region could focus sampling efforts in the dry season, where detectability of target species is higher, while assuring data collection for at least three years. More generally, our study reveals the importance of long-term cave monitoring programs for detecting possible disturbances in subterranean ecosystems, and for using the generated information to optimize future monitoring efforts.

SUBMITTER: Trevelin LC

PROVIDER: S-EPMC8054738 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The Amazon is renowned worldwide for its biological significance, but it also harbors substantial mineral reserves. Among these, the ferruginous geosystems of the region are critical for iron ore extraction, accounting for 10% of Brazil's export revenue. Additionally, this region holds a significant speleological heritage with more than 1,000 caves. However, cave conservation efforts are often in conflict with land use, necessitating mediation through environmental regulations. While conservation decisions typically consider only the caves' characteristics, such an approach fails to account for the interactions among cave communities and their surrounding landscape. This poses a challenge to reserve design for cave conservation purposes. To address this issue, we assessed the predictors that influence the similarity among cave communities, suggesting the use of this parameter as a proxy for subterranean connectivity. Applying graph theory, we proposed a tool to aid in the selection of priority caves for conservation purposes. Our study involved the sampling of invertebrates in 69 iron ore caves and analyzing 28 environmental variables related to these subterranean habitats and adjacent landscape. Our analysis revealed that landscape and habitat characteristics are more important than geographical distance in determining patterns of similarity among caves. Our graph approach highlighted densely interconnected clusters based on similarity. However, specific caves stood out for harboring exclusive fauna and/or exhibiting habitat specificity, making them unique in the study area. Thus, we recommend prioritizing cave clusters for conservation, assembling both singular caves and others that influence them. It is crucial to note that protocols for the protection of subterranean biodiversity must consider measures that encompass both the caves and the surrounding landscape. Our methodology provides insights into the connectivity among caves, identifies existing groups, highlights singular (or unique) cavities that require preservation, and recognizes those influencing these unique habitats. This methodological advancement is crucial for the development of better conservation policies for the speleological heritage in areas under constant economic pressure.

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Optimizing speleological monitoring efforts: insights from long-term data for tropical iron caves.

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