Considering reservoirs as linear fragments in a basin's river network could improve understanding, predictability, and management efficiency. We looked for general cascading spatial patterns across five categories of reservoir attributes: land cover, morphology and hydrology, fish habitat, fish assemblages, and fisheries. Attributes were pulled from various databases for large reservoirs (>100 ha) located in the United States. 16 widely distributed river basins, each including a minimum of 15 large reservoirs, were selected for analysis. Using analysis of covariance with basin as the class variable, we tested each attribute as a linear function of catchment area, which is an index of reservoir position in the basin. The majority of reservoir attributes displayed log‐linear patterns as catchment area increased, indicating that reservoirs act as members of a larger network just as river reaches do. Several patterns were detected including attributes with no apparent lengthwise arrangement along the basin; cascading spatial patterns in which attributes increase or decrease from upstream to downstream within a basin; and attributes that increase with catchment area in some basins, decrease in others, or may simply remain constant throughout the basin. We conclude that each pattern may have different implications for management, and that the effectiveness with which most management activities influence reservoirs is likely to increase or decrease along river basins.

Increasing concern regarding environmental degradation in coastal areas that experience annual hypoxic zones has led to the need for mitigation of nutrient laden runoff from inland landscapes. An annual occurrence of a hypoxic zone in the Gulf of Mexico has led to the development and implementation of nutrient reduction strategies throughout the Mississippi River Basin (MRB). With federal, state, and private financial and technical assistance, landowners have implemented best management practices (BMPs) to combat nutrient and sediment nonpoint source pollution; however, the effectiveness of these BMPs alone or utilized together has not been quantified. This study uses a field-scale, paired watershed approach in two watersheds in the Mississippi Alluvial Valley to test for differences in sediment and nutrient runoff concentrations between four management systems. A total of 774 samples (415 baseflow samples and 359 stormflow samples) were collected from 2011 to 2015. Median baseflow concentrations across all sites within this study were 52 mg L⁻¹ for total suspended solids (TSS), 0.38 mg L⁻¹ for total phosphorus (TP), 0.09 mg L⁻¹ for nitrate-nitrite (NO₃⁻-NO₂⁻), and 0.81 mg L⁻¹ for ammonium (NH₄⁺). Median sediment and nutrient concentrations from stormflow samples across all sites within the study were greater than baseflow concentrations. Median stormflow concentrations across all sites were 985 mg L⁻¹ for TSS, 1.21 mg L⁻¹ for TP, 0.32 mg L⁻¹ for NO₃⁻-NO₂⁻, and 1.04 mg L⁻¹ for NH₄⁺. Results showed no strong improvements in water quality from agricultural landscapes where suites of BMPs had been implemented, rather the data showed variability in runoff concentrations indicative of strong influences from environmental and management variables. Study outcomes highlight opportunities to better capture nutrient dynamics at the field scale through adaptive management of BMPs for increased effectiveness of nonpoint source pollution reduction and monitoring.

In natural resource and environmental management, well‐written objectives are critical for effective decision‐making and the achievement of desired outcomes. This article aims to improve practitioners' ability to identify and write SMART (Specific, Measurable, Achievable, Relevant, Time‐bound) objectives for natural resource and environmental management. We differentiate between research and management objectives, define each SMART criterion, and introduce a template to help write management objectives. We demonstrate the use of the SMART management objectives template with a hypothetical example and two recent applications. The template simplifies the process of writing SMART management objectives. It is applicable to popular management frameworks like management by objectives and structured decision making. The implementation of SMART criteria and the provided template can lead to better outcomes in natural resource and environmental management, benefiting current and future generations.

Rock outcrops serve as unique habitats for several specialist species, yet their identification and conservation are challenged due to logistical and computational constraints. This study aimed to improve the accuracy and efficiency of locating sheer rock outcrops (>60cm in height) using high- and medium-resolution Digital Elevation Models (DEM) and Conditional Inference Trees, a recursive binary partitioning approach to predict an outcome. In our application, the outcome is the presence of sheer rock outcrops. We used slope and topographic position index (TPI) as predictors. The Conditional Inference Tree models estimated slope thresholds of 38.4° for high-resolution DEM and 18.14° for medium-resolution DEM as indicative of sheer rock outcrop presence. These findings align with a previous study, adding a layer of validity. Additionally, a TPI >0.137 in the high-resolution DEM provided additional accuracy of sheer rock outcrop presence and the medium-resolution (∼10m) DEM can be used when high-resolution (1m) DEM is not available. This is particularly relevant for large-scale conservation efforts where computational resources may be limited. However, high-resolution DEM is recommended for more precise quantification of the total area of sheer rock outcrops. Our study has significant implications for efficiently locating sheer rock outcrops and identifying putative areas for protection to help conserve species that rely on these unique habitats, while minimizing the logistical constraints of previous methods. The modular methodology developed here could serve as an invaluable approach for broader rock outcrop conservation. Furthermore, our findings add momentum to further innovate remote sensing of unique habitats.