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.

Performance of National Maps of Watershed Integrity at Watershed Scales.

Watershed integrity, the capacity of a watershed to support and maintain ecological processes essential to the sustainability of services provided to society, can be influenced by a range of landscape and in-stream factors. Ecological response data from four intensively monitored case study watersheds exhibiting a range of environmental conditions and landscape characteristics across the United States were used to evaluate the performance of a national level Index of Watershed Integrity (IWI) at regional and local watershed scales. Using Pearson's correlation coefficient (r), and Spearman's rank correlation coefficient (rs ), response variables displayed highly significant relationships and were significantly correlated with IWI and ICI (Index of Catchment Integrity) values at all watersheds. Nitrogen concentration and flux-related watershed response metrics exhibited significantly strong negative correlations across case study watersheds, with absolute correlations (|r|) ranging from 0.48 to 0.97 for IWI values, and 0.31 to 0.96 for ICI values. Nitrogen-stable isotope ratios measured in chironomids and periphyton from streams and benthic organic matter from lake sediments also demonstrated strong negative correlations with IWI values, with |r| ranging from 0.47 to 0.92, and 0.35 to 0.89 for correlations with ICI values. This evaluation of the performance of national watershed and catchment integrity metrics and their strong relationship with site level responses provides weight-of-evidence support for their use in state, local and regionally focused applications.

Past climate change drives current genetic structure of an endangered freshwater mussel species.

Historical-to-recent climate change and anthropogenic disturbance affect species distributions and genetic structure. The Rio Grande watershed of the United States and Mexico encompasses ecosystems that are intensively exploited, resulting in substantial degradation of aquatic habitats. While significant anthropogenic disturbances in the Rio Grande are recent, inhospitable conditions for freshwater organisms likely existed prior to such disturbances. A combination of anthropogenic and past climate factors may contribute to current distributions of aquatic fauna in the Rio Grande basin. We used mitochondrial DNA and 18 microsatellite loci to infer evolutionary history and genetic structure of an endangered freshwater mussel, Popenaias popeii, throughout the Rio Grande drainage. We estimated spatial connectivity and gene flow across extant populations of P. popeii and used ecological niche models (ENMs) and approximate Bayesian computation (ABC) to infer its evolutionary history during the Pleistocene. structure results recovered regional and local population clusters in the Rio Grande. ENMs predicted drastic reductions in suitable habitat during the last glacial maximum. ABC analyses suggested that regional population structure likely arose in this species during the mid-to-late Pleistocene and was followed by a late Pleistocene population bottleneck in New Mexico populations. The local population structure arose relatively recently, perhaps due to anthropogenic factors. Popenaias popeii, one of the few freshwater mussel species native to the Rio Grande basin, is a case study for understanding how both geological and anthropogenic factors shape current population genetic structure. Conservation strategies for this species should account for the fragmented nature of contemporary populations.