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Ecology is often governed by nonlinear dynamics. Nonlinear ecological relationships can include thresholds—incremental changes in drivers that provoke disproportionately large ecological responses. Among the species that experience nonlinear and threshold dynamics are Pacific salmon (Oncorhynchus spp.). These culturally, ecologically, and economically significant fishes are in many places declining and management focal points. Often, managers can influence or react to ecological conditions that salmon experience, suggesting that nonlinearities, especially thresholds, may provide opportunities to inform decisions. However, nonlinear dynamics are not always invoked in management decisions involving salmon. Here, we review reported nonlinearities and thresholds in salmon ecology, describe potential applications that scientists and managers could develop to leverage nonlinear dynamics, and offer a path toward decisions that account for ecological nonlinearities and thresholds to improve salmon outcomes. It appears that nonlinear dynamics are not uncommon in salmon ecology and that many management arenas may potentially leverage them to enable more effective or efficient decisions. Indeed, decisions guided by nonlinearities and thresholds may be particularly desirable considering salmon management arenas are often characterized by limited resources and mounting ecological stressors, practical constraints, and conservation challenges. More broadly, many salmon systems are data-rich and there are an extensive range of ecological contexts in which salmon are sensitive to anthropogenic decisions. Approaches developed to leverage nonlinearities in salmon ecology may serve as examples that may inform analogous approaches in other systems and taxa.

Estimating trends in abundance from fishery catch rates is one of the oldest endeavors in fisheries science. However, many jurisdictions do not analyze fishery catch rates due to concerns that these data confound changes in fishing behavior (adjustments in fishing location or gear operation) with trends in abundance. In response, we developed a spatial dynamic factor analysis (SDFA) model that decomposes covariation in multispecies catch rates into components representing spatial variation and fishing behavior. SDFA estimates spatiotemporal variation in fish density for multiple species and accounts for fisher behavior at large spatial scales (i.e., choice of fishing location) while controlling for fisher behavior at fine spatial scales (e.g., daily timing of fishing activity). We first use a multispecies simulation experiment to show that SDFA decreases bias in abundance indices relative to ignoring spatial adjustments and fishing tactics. We then present results for a case study involving petrale sole (Eopsetta jordani) in the California Current, for which SDFA estimates initially stable and then increasing abundance for the period 1986-2003, in accordance with fishery-independent survey and stock assessment estimates. L'estimation des tendances d'abondance a partir de taux de prises des peches est une des pratiques les plus anciennes des sciences halieutiques. De nombreux pays n'analysent cependant pas les taux de prises des peches parce que ces donnees pourraient ne pas distinguer les modifications des comportements de peche (ajustements du lieu de peche ou de l'utilisation d'engins) des tendances d'abondance. Nous avons donc developpe un modele d'analyse des facteurs de dynamique spatiale (SDFA) qui decompose la covariation des taux de prises multiespeces en differentes composantes representant la variation spatiale et le comportement de peche. La SDFA estime les variations spatiotemporelles de densite de poissons pour des especes multiples et tient compte du comportement de peche a de grandes echelles spatiales (c.-a-d., le choix du lieu de peche), tout en controlant pour le comportement des pecheurs a petites echelles spatiales (p. ex. l'heure du jour de l'activite de peche). Nous utilisons d'abord une experience de simulation multiespeces pour demontrer que la SDFA reduit le biais dans les indices d'abondance par rapport a la non-prise en compte des ajustements spatiaux et des tactiques de peche. Nous presentons ensuite les resultats pour une etude de cas portant sur la platija prtrale (Eopsetta jordani) dans le courant de Californie, pour laquelle la SDFA estime une abondance initialement stable, puis croissante pour la periode 1986-2002, ce qui concorde avec les estimations tirees devaluations independantes de la peche et devaluations des stocks. [Traduit par la Redaction], Introduction Many developed and developing countries collect detailed information from fishers regarding the location, timing, and catches from fishing operations. This fishing information is typically used to estimate total catch [...]

Habitat restoration efforts are often conducted under limited budgets and information. Explicitly incorporating data on project costs can improve the effectiveness of restoration investments but gathering precise data for future projects is often infeasible at the planning stage. We demonstrate econometric and machine learning methods for improving the accessibility of cost information for restoration planning. Using geospatial matching methods, we combine 15 years of spatially explicit project records for over 1200 culvert fish passage barrier restoration worksites across the Pacific Northwest of the United States with data layers of hypothesized drivers of construction costs. We distinguish between two objectives in analyzing cost data: inference to identify cost drivers and prediction of future project costs. For inference, we multiple linear regression and find that the variables channel bankfull width and slope, road speed class, developed and agricultural land covers, and proximity to privately managed industrial land are associated with culvert restoration costs and may serve as strong proxies in a planning setting. For prediction, we use boosted regression trees to make out-of-sample projections of restoration costs for over 27,000 barrier culverts documented in state inventories. The distribution of these cost projections over space and across jurisdictions reveal higher, and more heterogeneous, culvert restoration costs in the Puget Sound region than in other nearby areas. Our results are directly applicable for resource managers making fish passage restoration decisions and serve as a template for the use of econometrics and machine learning to analyze costs recorded in restoration project databases in other contexts. [ABSTRACT FROM AUTHOR]