Systematic conservation planning has been used extensively throughout the world to identify important areas for maintaining biodiversity and functional ecosystems, and is well suited to address large-scale biodiversity conservation challenges of the twenty-first century. Systematic planning is necessary to bridge implementation, scale, and data gaps in a collaborative effort that recognizes competing land uses. Here, we developed a conservation planning process to identify and unify conservation priorities around the central and southern Appalachian Mountains as part of the Appalachian Landscape Conservation Cooperative (App LCC). Through a participatory framework and sequential, cross-realm integration in spatial optimization modeling we highlight lands and waters that together achieve joint conservation goals from LCC partners for the least cost. This process was driven by a synthesis of 26 multi-scaled conservation targets and optimized for simultaneous representation inside the program Marxan to account for roughly 25% of the LCC geography. We identify five conservation design elements covering critical ecological processes and patterns including interconnected regions as well as the broad landscapes between them. Elements were then subjected to a cumulative threats index for possible prioritization. The evaluation of these elements supports multi-scaled decision making within the LCC planning community through a participatory, dynamic, and iterative process.

Since changes in climate and land use operate at broad spatial scales, efficient monitoring of temporal trends in fisheries resources over large geographic areas is vital to appropriate management. We compared the statistical power of single‐ versus three‐pass electrofishing surveys in detecting temporal trends of age‐1 and older Brook Trout Salvelinus fontinalis populations in western North Carolina. Empirical estimates of abundance and capture probabilities were obtained from annual three‐pass depletion surveys at 14 headwater stream sites between 2012 and 2017. The CVs in abundance averaged 26% (SD = 14.2%) across study sites, and the mean capture probability per pass was 0.72 (range = 0.57–0.84, SD = 0.09). Captures from single‐pass sampling and abundance estimates from three‐pass removal sampling were highly correlated (r2 = 0.98). In simulations, under the range of years sampled (5–25 years) and annual declines (2.5–7.5%) considered, the power to detect temporal trends was similar (∆ power < 0.1) between the two methods when five or more sites were monitored. An additional set of simulations with varying capture probabilities demonstrated that differences in power between the two methods increased as mean capture probabilities decreased (0.8, 0.5, and 0.2) accompanied by larger variation in capture probabilities among the samples, indicating results obtained with Brook Trout populations in North Carolina might not be applicable to other habitat types or species. Variation in fish abundance did not affect the difference in power between the two methods. Single‐pass electrofishing surveys can be an efficient survey method to monitor temporal population trends for habitat types and species characterized with high capture probabilities and low variation among samples. However, single‐pass data would not typically allow for inferences of capture probabilities and thus abundance. This can be problematic when environmental factors vary and when data sets collected using different protocols are compared. This trade‐off should be carefully considered when designing monitoring programs.

Threats to aquatic biodiversity are expressed at broad spatial scales, but identifying regional trends in abundance is challenging owing to variable sampling designs and temporal and spatial variation in abundance. We compiled a regional data set of brook trout (Salvelinus fontinalis) counts across their southern range representing 326 sites from eight states between 1982 and 2014 and conducted a statistical power analysis using Bayesian state-space models to evaluate the ability to detect temporal trends by characterizing posterior distributions with three approaches. A combination of monitoring periods, number of sites and electrofishing passes, decline magnitude, and different revisit patterns were tested. Power increased with monitoring periods and decline magnitude. Trends in adults were better detected than young-of-the-year fish, which showed greater interannual variation in abundance. The addition of weather covariates to account for the temporal variation increased power only slightly. Single- and three-pass electrofishing methods were similar in power. Finally, power was higher for sampling designs with more frequent revisits over the duration of the monitoring program. Our results provide guidance for broad-scale monitoring designs for temporal trend detection.

Model systems enlightened by history that provide understanding and inform contemporary and future landscapes are needed. Through transdisciplinary collaboration, historic rice fields of the southeastern United States can be such models, providing insight into how human–ecological systems work. Rice culture in the United States began in the 1670s; was primarily successfully developed, managed, and driven by the labor of enslaved persons; and ended with the U.S. Civil War. During this time, wetlands were transformed into highly managed farming systems that left behind a system of land use legacies when abandoned after slavery. Historically accepted estimates range from 29,950 to 60,703 ha; however, using remotely sensed data (e.g., LiDAR) and expert opinion, we mapped 95,551 ha of historic rice fields in South Carolina, USA. After mapping, the rice fields’ current wetland and land cover characteristics were assessed. Understanding the geographic distribution and characteristics allows insight into the overall human and ecological costs of forced land use change that can inform future landscapes.

Brook Trout Salvelinus fontinalis in southern Appalachian Mountains streams of the USA occur at the southernmost portion of their native range, and occupy small, isolated, and low-productivity headwater streams. The existing standard weight (Ws) equation is applicable only to Brook Trout &gt; 120 mm total length (TL), but many individuals in the region are smaller than this minimum size threshold due to their habitat characteristics. Here, we developed a new Ws equation for Brook Trout in southern Appalachian Mountains streams using length-weight data on 72,502 individuals. The weighted quadratic empirical-percentile method minimized length-related bias in relative weight compared to the regression-line-percentile and weighted linear empirical-percentile methods. The proposed Ws equation was: log10W = -3.364 + 1.378 x log10L + 0.397 x (log10L)2, where W was weight (g) and L was TL (mm). The new equation characterized body condition of Brook Trout in southern Appalachian Mountains streams more accurately than the existing equation.