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Stream classification plays an important role in the study and management of freshwater ecosystems. Many classification schemes exist that focus predominantly on physical habitat, hydrology, and thermal regimes, but few frameworks explicitly include river network connectivity. Because river network connectivity directly affects the movement of water, nutrients, sediments, and aquatic species throughout a watershed, including connectivity metrics in river classification provides opportunities for advancing riverine research at large spatial extents. We developed a robust framework and dataset that incorporates a network connectivity-based stream classification system for the conterminous United States (NetConUS), utilizing the National Hydrography Dataset Plus version 2 (NHD). Connectivity classes are differentiated by degree centrality, eigen-vector centrality, clustering co-efficient, closeness centrality and betweenness centrality and comprise five types: central streams, peripheral streams, mainstem streams, cluster stream and convergent streams. The streams classification was validated using Bayesian Neural Networks (BNNs) to account for uncertainty in the assignment of network connectivity classes. The dataset captures the structural roles of stream segments within river networks and provides opportunities to include network connectivity metrics alongside geophysical descriptors in analyses of freshwater fauna at the extent of the conterminous US. 

Abstract Trophic interactions operate across the lifetime of an individual organism, yet our understanding of these processes is largely limited to a single life stage or moment in time. Management and conservation implications of this knowledge gap are particularly important, given the mounting number, spread, and ecological impacts of invasive species. Biotracers, such as carbon and nitrogen stable isotopes of animal muscle, are commonly used to characterize the trophic ecology of an individual but fail to capture intraindividual variation and ontogenetic dietary shifts. However, recent work suggests that eye lenses may facilitate the reconstruction of individual lifetime trophic trajectories for fishes, including the chronology of past trophic positions of and carbon flow to consumers. By combining stable isotope analysis of fish eye lens tissue with aging techniques (otolith growth measurements), this study is the first to ask how the lifetime trophic niches of individuals vary within different community contexts. The results provide evidence for asymmetric competition causing differing trajectories in lifetime trophic niches for native and nonnative fishes along an invasion gradient in Burro Creek, Arizona, USA. Native roundtail chub, Sonora sucker, and desert sucker all displayed a coordinated displacement of lifetime trophic trajectories to a lower trophic level and reliance on aquatic, rather than terrestrial, resources as indicated by a shift to lower δ¹³C and δ¹⁵N in mixed, relative to native‐only, communities. By contrast, the trophic trajectories of nonnative green sunfish and bullhead species remained consistent between native and nonnative dominated communities. The presence of nonnative species led to a significantly greater decrease in δ¹³C through ontogeny for roundtail chub, a species of conservation concern in Arizona. These results demonstrate the prolonged trophic impact of nonnative fishes on native fishes beyond a single life stage. Displacement of ontogenetic dietary shifts by native fishes through interactions with nonnative species may lead to reduced fish growth and fitness, with implications at the population and ecosystem levels. Stable isotope analysis of fish eye lens tissue offers new opportunities to study the lifetime chronology of individual feeding habits and allows for exploration of the impacts of invasive species and environmental change throughout ontogeny. 

Abstract The global spread of invasive species in aquatic ecosystems has prompted population control efforts to mitigate negative impacts on native species and ecosystem functions. Removal programs that optimally allocate removal effort across space and time offer promise for improving invader suppression or eradication, especially given the limited resources available to these programs. However, science‐based guidance to inform such programs remains limited. This study leverages two intensive fish removal programs for nonnative green sunfish (Lepomis cyanellus) in intermittent streams of the Bill Williams River basin in Arizona, USA, to explore alternative management strategies involving variable allocation of removal effort in time and space and compare static versus dynamic decision rules. We used Bayesian hierarchical modeling to estimate demographic parameters using existing removal data, with evidence that both removal programs led to at least a 0.39 probability of eradication. Simulated alternative management strategies revealed that population suppression, but not eradication, could be achieved with reduced effort and that dynamic management practices that respond to species abundance in real time can improve the efficiency of removal efforts. High removal frequency and program duration, including continued monitoring after zero fish were captured, contributed to successful population control. With management efforts struggling to keep pace with the rising spread and impacts of invasive species, this research demonstrates the utility of quantitative removal models to help improve invasive removal programs and robustly evaluate the success of population suppression and eradication. 

Many well‐supported hypotheses seek to explain drivers of nonnative species richness across spatial scales, but evidence for common patterns among regions and taxa remains inconclusive. This study investigates why consistent patterns are elusive by estimating and assessing cross‐scale interactions, wherein large‐scale factors contextualize patterns measured at smaller scales. We investigated whether local relationships of disturbance and native species richness with nonnative species richness are moderated by regional native gamma diversity. Regions with higher gamma diversity, we hypothesized, would be unfavorable to nonnative species due to high levels of competition and reduced niche availability, thus mediating local effects of native richness and disturbance on nonnative species richness. Using a fine resolution stream fish community dataset covering 159 regional watersheds in the conterminous United States during 2000–2023, we quantified cross‐scale interactions using a two‐level Bayesian hierarchical model. In the first level, we estimated the effects of disturbance and native richness on nonnative richness in local stream segments indexed by region. In the second level, we used this regional index to estimate cross‐scale interactions of native gamma diversity (regional‐level richness) on the first‐level relationships. Local nonnative richness was generally positively associated with native richness and disturbance. However, these relationships were reduced in regions with more diverse native stream fish assemblages. Thus, native gamma diversity provided an important mechanistic context for local nonnative richness relationships across regional watersheds through a negative cross‐scale interaction. As large spatial datasets become increasingly available, accounting for cross‐scale interactions in inter‐regional observational studies will be critical for understanding ecological relationships and may provide a predictive framework for studies with conflicting support for differing conceptual models. 

Abstract BackgroundWhile the adverse health effects of civil aircraft noise are relatively well studied, impacts associated with more intense and intermittent noise from military aviation have been rarely assessed. In recent years, increased training at Naval Air Station Whidbey Island, USA has raised concerns regarding the public health and well-being implications of noise from military aviation. ObjectiveThis study assessed the public health risks of military aircraft noise by developing a systematic workflow that uses acoustic and aircraft operations data to map noise exposure and predict health outcomes at the population scale. MethodsAcoustic data encompassing seven years of monitoring efforts were integrated with flight operations data for 2020–2021 and a Department of Defense noise simulation model to characterize the noise regime. The model produced contours for day-night, nighttime, and 24-h average levels, which were validated by field monitoring and mapped to yield the estimated noise burden. Established thresholds and exposure-response relationships were used to predict the population subject to potential noise-related health effects, including annoyance, sleep disturbance, hearing impairment, and delays in childhood learning. ResultsOver 74,000 people within the area of aircraft noise exposure were at risk of adverse health effects. Of those exposed, substantial numbers were estimated to be highly annoyed and highly sleep disturbed, and several schools were exposed to levels that place them at risk of delay in childhood learning. Noise in some areas exceeded thresholds established by federal regulations for public health, residential land use and noise mitigation action, as well as the ranges of established exposure-response relationships. Impact statementThis study quantified the extensive spatial scale and population health burden of noise from military aviation. We employed a novel GIS-based workflow for relating mapped distributions of aircraft noise exposure to a suite of public health outcomes by integrating acoustic monitoring and simulation data with a dasymetric population density map. This approach enables the evaluation of population health impacts due to past, current, and future proposed military operations. Moreover, it can be modified for application to other environmental noise sources and offers an improved open-source tool to assess the population health implications of environmental noise exposure, inform at-risk communities, and guide efforts in noise mitigation and policy governing noise legislation, urban planning, and land use. 

Abstract Land use intensification has led to conspicuous changes in plant and animal communities across the world. Shifts in trait‐based functional composition have recently been hypothesized to manifest at lower levels of environmental change when compared to species‐based taxonomic composition; however, little is known about the commonalities in these responses across taxonomic groups and geographic regions. We investigated this hypothesis by testing for taxonomic and geographic similarities in the composition of riverine fish and insect communities across gradients of land use in major hydrological regions of the conterminous United States. We analyzed an extensive data set representing 556 species and 33 functional trait modalities from 8023 fish communities and 1434 taxa and 50 trait modalities from 5197 aquatic insect communities. Our results demonstrate abrupt threshold changes in both taxonomic and functional community composition due to land use conversion. Functional composition consistently demonstrated lower land use threshold responses compared to taxonomic composition for both fish (urbanp = 0.069; agriculturep = 0.029) and insect (urbanp = 0.095; agriculturep = 0.043) communities according to gradient forest models. We found significantly lower thresholds for urban versus agricultural land use for fishes (taxonomic and functionalp < 0.001) and insects (taxonomicp = 0.001; functionalp = 0.033). We further revealed that threshold responses in functional composition were more geographically consistent than for taxonomic composition to both urban and agricultural land use change. Traits contributing the most to overall functional composition change differed along urban and agricultural land gradients and conformed to predicted ecological mechanisms underpinning community change. This study points to reliable early‐warning thresholds that accurately forecast compositional shifts in riverine communities to land use conversion, and highlight the importance of considering trait‐based indicators of community change to inform large‐scale land use management strategies and policies. 

The abundant‐center hypothesis (ACH) provides a conceptual model for predicting range‐wide distributions of species abundance, suggesting that abundance peaks in the center of the geographic range and declines towards range edges. Empirical studies testing the ACH and its subsequent derivations predominantly occurred in terrestrial systems and reported mixed support. Moreover, none of these models consider the possibility of multiple geographic areas of elevated abundance (which we refer to as ‘abundant cores'). Naturally dispersal limited species may exhibit multiple abundant cores, requiring refinement of the ACH. We used fish species abundances from 29 206 community monitoring surveys and weighted geospatial kernel density estimation to identify the number of abundant cores for 64 freshwater fish species. We regressed the number of abundant cores against range size and body size to test if larger geographic distributions and body sizes contain more abundant cores than smaller distributions and body sizes. The two predictors are surrogates for evolutionary age and dispersal ability, respectively, because older species are generally associated with larger ranges, and large‐bodied fishes have greater dispersal ability than small‐bodied fishes in dendritic networks. For studied species, 43 exhibited multi‐core distributions, and 21 exhibited a single‐core distribution. Species range size, but not body size, was significantly and positively associated with the number of abundant cores. The ACH was not a good descriptor of the abundance patterns of most stream fishes we studied, suggesting that an abundant center model may not be well‐suited for freshwater fishes. Recent geo‐climatic events in evolutionary time have isolated populations of the same species by a matrix of unsuitable habitat and/or hard dispersal barriers, providing the basis for multi‐core distributions. Biogeographic and ecological mechanisms likely underpin observed multi‐core patterns, and our work indicates that the ACH and related concepts still present opportunities for testing and refinement. 

ABSTRACT AimEmpirical tests of conceptual hypotheses describing species invasions often differ depending on the spatial scale (spatial resolution and extent of study area) at which they were conducted. Some of this disparity may arise from tradeoffs in data quality necessitating the use of different indices of community invadedness among scales. Local‐scale studies typically use fine‐resolution, descriptive measures of community invadedness (‘dominance’, the proportion nonnative individuals) at limited spatial extents, while macroscale studies often aggregate datasets to cover large spatial extents but use coarser spatial resolution and less descriptive indices (nonnative species richness). We investigated the consequences of using different indices to represent community invadedness at different spatial scales, and explored the implications for hypothesis testing when nonnative richness and dominance are not related. Location23,793 stream segments within 17 regional watersheds, conterminous United States. Time Period2000–2023. Major Taxa StudiedFreshwater fishes. MethodsUsing a large‐extent, fine‐resolution dataset, we evaluated the correlation between nonnative species richness and dominance in communities, and compared empirical support for prominent invasion hypotheses (biotic resistance, disturbance facilitation) in identical Bayesian hierarchical models with community invadedness represented by each metric. ResultsNonnative richness and dominance were weakly correlated, allowing us to classify communities into four archetypes based on relationships between the two indices. Empirical support for both invasion hypotheses differed between the two indices of community invadedness both overall and within regional watersheds. Main ConclusionsNonnative species richness and dominance describe different facets of the invasion process and may under‐ or over‐represent community invadedness when considered alone. Empirical disparity between models estimating the two metrics may be an important source of scale‐dependent inference in invasion ecology. When assembling datasets for macroscale studies, retaining fine spatial resolution as much as possible will allow researchers opportunities to use more descriptive and potentially complementary indices of community invadedness.