Preparing GIS data for analysis of stream monitoring data: The R package openSTARS

Modeling nature-based restoration potential across aquatic-terrestrial boundaries

Today, few watersheds remain untouched by global change processes arising from climate warming, impoundments, channelization, water extraction, pollution, and urbanization. The need for restoration has resulted in a myriad of interventions, generally performed at small scales, which have limited measurable impact in restoring biodiversity and ecosystem functions. We propose bringing nature-based restoration (also referred to as rewilding) principles to rivers and their watersheds to allow freshwater ecosystems to heal themselves and present a case study example for the Wolastoq, a transboundary watershed on North America's east coast. We aimed to identify key areas for the provision of the ecosystem function secondary productivity in the watershed and explored how the existing network of protected lands contributes to its conservation. We first developed species distribution models for 94 aquatic insects and 5 aerial insectivores and then considered human footprint and existing protected areas when employing spatial prioritization to meet 2 area-based targets (17% and 30% [i.e., Aichi Biodiversity Target 11 and Canada's 30×30, respectively]) for conservation or restoration of freshwater secondary production. Current conservation protection in the watershed was predicted to be insufficient to protect either ecosystem function providers or receivers of secondary production. By considering integrated conservation strategies, restoration and conservation actions can be better allocated throughout habitat patches to ensure sustained provision of ecosystem functions across the watershed. Nature-based restoration and conservation can help inform Canada's area-based targets, providing a framework for incorporating ecosystem functions into conservation planning and offering practical insights for policy and restoration efforts aimed at safeguarding biodiversity.

Niche partitioning in a periphyton metacommunity peaks at intermediate species richness in midsized rivers

The trait-based partitioning of species plays a critical role in biodiversity-ecosystem function relationships. This niche partitioning drives and depends on community structure, yet this link remains elusive in the context of a metacommunity, where local community assembly is dictated by regional dispersal alongside local environmental conditions. Hence, elucidating the coupling of niche partitioning and community structure needs spatially explicit studies. Such studies are particularly necessary in river networks, where local habitats are highly connected by unidirectional water flow in a spatially complex network structure and frequent disturbance makes community structure strongly dependent on recolonization. Here, we show that taxonomic turnover among periphyton communities colonizing deployed bricks (microhabitats) at multiple sampling sites (local habitats) in a river network came along with a turnover in traits. This niche partitioning showed a hump-shaped relationship with richness of periphyton communities, which increased along river size. Our observations suggest downstream dispersal along the river network to increase the regional metacommunity pool, which then ensures local colonization by taxa possessing diverse traits allowing them to efficiently partition into environmentally different microhabitats. However, at the most downstream sites, the excessive dispersal of widespread generalists drove mass effects which inflated richness with taxa that co-occupied several microhabitats and swamped niche partitioning. Further, efficient niche partitioning depended on communities rich in rare taxa, an indication for the importance of specialists. Alarmingly, richness and rare taxa declined with high phosphorus concentrations and conductivity, respectively, two environmental variables which potentially reflected anthropogenic activity.

Neglecting biodiversity baselines in longitudinal river connectivity restoration impacts priority setting

River habitats are fragmented by barriers which impede the movement and dispersal of aquatic organisms. Restoring habitat connectivity is a primary objective of nature conservation plans with multiple efforts to strategically restore connectivity at local, regional, and global scales. However, current approaches to prioritize connectivity restoration do not typically consider how barriers spatially fragment species' populations. Additionally, we lack knowledge on biodiversity baselines to predict which species would find suitable habitat after restoring connectivity. In this paper, we asked how neglecting these biodiversity baselines in river barrier removals impacts priority setting for conservation planning. We applied a novel modelling approach combining predictions of species distributions with network connectivity models to prioritize conservation actions in rivers of the Rhine-Aare system in Switzerland. Our results show that the high number and density of barriers has reduced structural and functional connectivity across representative catchments within the system. We show that fragmentation decreases habitat suitability for species and that using expected distributions as biodiversity baselines significantly affects priority settings for connectivity restorations compared to species-agnostic metrics based on river length. This indicates that priorities for barrier removals are ranked higher within the expected distributions of species to maximize functional connectivity while barriers in unsuitable regions are given lower importance scores. Our work highlights that the joint consideration of existing barriers and species past and current distributions are critical for restoration plans to ensure the recovery and persistence of riverine fish diversity.

A framework for validating watershed ecosystem service models in the United States using long-term water quality data: Applications with the InVEST Nutrient Delivery (NDR) model in Puerto Rico

Modeling of watershed Ecosystem Services (ES) processes has increased greatly in recent years, potentially improving environmental management and decision-making by describing the value of nature. ES models may be sensitive to different conditions and, therefore, should ideally be validated against observed data for their use as a decision-support instrument. However, outcomes from such ES modeling are barely validated, making it difficult to assess uncertainties associated with the modeling and justify their actual usefulness to develop generalizable management recommendations. This study proposes a framework for the systematic validation of one of such tools, the InVEST Nutrient Delivery Model (NDR) for nutrient retention estimates. The framework is divided into three stages: 1) running the NDR model inputs, processes, and outputs; 2) building a long-term reference dataset from open access water quality observations; and 3) using the reference data for model calibration and validation. We applied this framework to twenty watersheds in the Commonwealth of Puerto Rico, where data availability resembles thar of watersheds across the United States. Long-term water quality data from monitoring stations facilitated model calibration and validation. Our framework provided a reproducible method to linking the vast monitoring network in the U.S. and its territories for evaluating the InVEST's NDR model performance. Beyond the framework development, this study found that the InVEST NDR model explained 62.7 % and 79.3 % of the variance in the total nitrogen and total phosphorus between 2000 and 2022, respectively, supporting the suitability of the model for watershed scale ecosystem services assessment. The findings can also serve as a reference to support the use of InVEST for other locations in the tropics without publically available monitoring data.

SSN2: The next generation of spatial stream network modeling in R

The SSN2 R package provides tools for spatial statistical modeling, parameter estimation, and prediction on stream (river) networks. SSN2 is the successor to the SSN R package (Ver Hoef, Peterson, Clifford, & Shah, 2014), which was archived alongside broader changes in the R-spatial ecosystem (Nowosad, 2023) that included 1) the retirement of rgdal (Bivand, Keitt, & Rowlingson, 2021), rgeos (Bivand & Rundel, 2020), and maptools (Bivand & Lewin-Koh, 2021) and 2) the lack of active development of sp (Bivand, Pebesma, & Gómez-Rubio, 2013). SSN2 maintains compatibility with the input data file structures used by the SSN R package but leverages modern R-spatial tools like sf (Pebesma, 2018). SSN2 also provides many useful features that were not available in the SSN R package, including new modeling and helper functions, enhanced fitting algorithms, and simplified syntax consistent with other R generic functions.

Species richness and food-web structure jointly drive community biomass and its temporal stability in fish communities

Biodiversity-ecosystem functioning and food-web complexity-stability relationships are central to ecology. However, they remain largely untested in natural contexts. Here, we estimated the links among environmental conditions, richness, food-web structure, annual biomass and its temporal stability using a standardised monitoring dataset of 99 stream fish communities spanning from 1995 to 2018. We first revealed that both richness and average trophic level are positively related to annual biomass, with effects of similar strength. Second, we found that community stability is fostered by mean trophic level, while contrary to expectation, it is decreased by species richness. Finally, we found that environmental conditions affect both biomass and its stability mainly via effects on richness and network structure. Strikingly, the effect of species richness on community stability was mediated by population stability rather than synchrony, which contrasts with results from single trophic communities. We discuss the hypothesis that it could be a characteristic of multi-trophic communities.