Geology topics

Alexander Flecker

Publications and source records attributed to Alexander Flecker.

6 recordsLinked to original sources

USGS2025

Sounds of Atlantic sturgeon spawning: First description and opportunities for riverine endangered species conservation with passive acoustic monitoring

Effective recovery plans for endangered species rely on insights into species’ ecology to identify risks and develop population recovery strategies. Data gaps pose challenges for many species of conservation concern, particularly those with cryptic behaviors or that occupy difficult-to-access habitats. Sounds produced by these species offer an effective means of observing many such marine and aquatic species, and for this reason, passive acoustic monitoring has emerged as an important study and assessment approach in marine systems. This approach is only just beginning to be applied for aquatic species monitoring in freshwater habitats. Atlantic sturgeon Acipenser oxyrinchus , a species of conservation concern along the US East Coast, remains poorly understood due to persistent data gaps despite years of conservation efforts. While sounds have been described for other sturgeons, sounds from Atlantic sturgeon have not yet been reported. Here, we characterized acoustic cues associated with Atlantic sturgeon in the Hudson River, New York, USA, and identified a low-frequency (44 Hz peak frequency) signal strongly correlated with the occurrence of telemetry-tagged adults which enter the river to spawn. We corroborated these efforts with recordings of captive Atlantic sturgeon, in which we detected the same sound type during a spawning period. Our findings provide an opportunity to develop passive acoustic monitoring strategies for Atlantic sturgeon, offering a non-invasive tool for understanding the spatiotemporal distribution of spawning activity across their range. We demonstrate potential applications of passive acoustic monitoring to inform sturgeon conservation and management, including characterizing habitat use, identifying cross-species interactions, and providing abundance indices.

New York↗

USGS2024

Human activities shape global patterns of decomposition rates in rivers

Rivers and streams contribute to global carbon cycling by decomposing immense quantities of terrestrial plant matter. However, decomposition rates are highly variable and large-scale patterns and drivers of this process remain poorly understood. Using a cellulose-based assay to reflect the primary constituent of plant detritus, we generated a predictive model (81% variance explained) for cellulose decomposition rates across 514 globally distributed streams. A large number of variables were important for predicting decomposition, highlighting the complexity of this process at the global scale. Predicted cellulose decomposition rates, when combined with genus-level litter quality attributes, explain published leaf litter decomposition rates with high accuracy (70% variance explained). Our global map provides estimates of rates across vast understudied areas of Earth and reveals rapid decomposition across continental-scale areas dominated by human activities.

Science↗

USGS2022

A machine learning approach to identify barriers in stream networks demonstrates high prevalence of unmapped riverine dams

Restoring stream ecosystem integrity by removing unused or derelict dams has become a priority for watershed conservation globally. However, efforts to restore connectivity are constrained by the availability of accurate dam inventories which often overlook smaller unmapped riverine dams. Here we develop and test a machine learning approach to identify unmapped dams using a combination of publicly available topographic and geospatial habitat data. Specifically, we trained a random forest classification algorithm to identify unmapped dams using digitally engineered predictor variables and known dam sites for validation. We applied our algorithm to two subbasins in the Hudson River watershed, USA , and quantified connectivity impacts, as well as evaluated a range of predictor sets to examine tradeoffs between classification accuracy and model parameterization effort. The random forest classifier achieved high accuracy in predicting dam sites (true positive rate = 89%, false positive rate = 1.2%) using a subset of variables related to stream slope and presence of upstream lentic habitats. Unmapped dams were prevalent throughout the two test watersheds. In fact, existing dam inventories underestimated the true number of dams by ∼80–94%. Accounting for previously unmapped dams resulted in a 62–90% decrease in dendritic connectivity indices for migratory fishes. Unmapped dams may be pervasive and can dramatically bias stream connectivity information. However, we find that machine learning approaches can provide an accurate and scalable means of identifying unmapped dams that can guide efforts to develop accurate dam inventories, thereby informing and empowering efforts to better manage them.

New York↗

USGS2021

Declining diversity of wild-caught species puts dietary nutrient supplies at risk

Although biodiversity loss adversely influences a variety of ecosystem functions, how declining wild food diversity affects nutrient supplies for people is poorly understood. Here, we analyze the impact of declining biodiversity on nutrients supplied by fish using detailed information from the Peruvian Amazon, where inland fisheries provide a critical source of nutrition for many of the region’s 800,000 people. We found that the impacts of biodiversity loss on nutrient supplies depended on compensation, trophic dynamics, and functional diversity. When small sedentary species compensated for declines in large migratory species, fatty acid supplies increased, while zinc and iron supplies decreased. In contrast, the probability of failing to maintain supplies or nutrient supply risk increased when species were nutritionally unique. Our results show that trait-based regulations and public health polices need to consider biodiversity’s vital role in sustaining nutritional benefits for over 2 billion people dependent on wild foods across the globe.

Science Advances↗

USGS2019

Computational sustainability: Computing for a better world and a sustainable future

Computational sustainability aims to develop computational methods to help solve environmental, economic, and societal problems and thereby facilitate a path towards a sustainable future. Sustainability problems are unique in scale, impact, complexity, and richness, offering challenges but also opportunities for the advancement of the state of the art of computing and information science.

Communications of the ACM↗

USGS2018

Efficiently approximating the Pareto frontier: Hydropower dam placement in the Amazon basin

Real–world problems are often not fully characterized by a single optimal solution, as they frequently involve multiple competing objectives; it is therefore important to identify the so-called Pareto frontier, which captures solution trade-offs. We propose a fully polynomial-time approximation scheme based on Dynamic Programming (DP) for computing a polynomially succinct curve that approximates the Pareto frontier to within an arbitrarily small > 0 on treestructured networks. Given a set of objectives, our approximation scheme runs in time polynomial in the size of the instance and 1/. We also propose a Mixed Integer Programming (MIP) scheme to approximate the Pareto frontier. The DP and MIP Pareto frontier approaches have complementary strengths and are surprisingly effective. We provide empirical results showing that our methods outperform other approaches in efficiency and accuracy. Our work is motivated by a problem in computational sustainability concerning the proliferation of hydropower dams throughout the Amazon basin. Our goal is to support decision-makers in evaluating impacted ecosystem services on the full scale of the Amazon basin. Our work is general and can be applied to approximate the Pareto frontier of a variety of multiobjective problems on tree-structured networks.

Conference Paper↗