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Mounting evidence shows overall insect abundances are in decline globally. Habitat loss, climate change, and pesticides have all been implicated, but their relative effects have never been evaluated in a comprehensive large-scale study. We harmonized 17 years of land use, climate, multiple classes of pesticides, and butterfly survey data across 81 counties in five states in the US Midwest. We find community-wide declines in total butterfly abundance and species richness to be most strongly associated with insecticides in general, and for butterfly species richness the use of neonicotinoid-treated seeds in particular. This included the abundance of the migratory monarch (Danaus plexippus), whose decline is the focus of intensive debate and public concern. Insect declines cannot be understood without comprehensive data on all putative drivers, and the 2015 cessation of neonicotinoid data releases in the US will impede future research. [ABSTRACT FROM AUTHOR]

Conservation planners often rely on heuristic indices when challenged with prioritizing potential projects under a constrained budget. This paper presents a comparative analysis of several prioritization indices (PIs) of culvert fish passage barriers, which can contribute to declines in anadromous fish populations. A federal injunction requires Washington state to restore 90% of habitat blocked by state‐owned culverts by 2030, prompting the development of numerous PIs, by various entities (i.e., counties, cities) within the injunction area. Our comparative analysis of PIs within the injunction Case Area investigates their ability to distinguish between barriers, their transferability in terms of scoring metrics, how scoring weights differ, and the preferences implied thereby. We document the use of six distinct PI methods by 10 entities and find that some PIs used many shared metrics, whereas others used a high percentage of unique metrics that would be difficult to replicate outside the entity's jurisdiction. Although habitat potential, habitat quantity, and connectivity were considered across all PIs, we found a high level of variation in terms of the metric weights. Our methods can be employed in other geographies or for other restoration PI planning efforts, and our results may facilitate the development and refinement of future PIs. [ABSTRACT FROM AUTHOR]

Conservation tillage in American soybean production has become increasingly common, improving soil health while reducing soil erosion and fuel consumption. This trend has been reinforced by the widespread adoption of glyphosate‐based weed control systems. Many weed species have since evolved to resist glyphosate, reducing its effectiveness. We provide evidence that the spread of glyphosate‐resistant weeds is responsible for significant reductions in the use of conservation tillage in soybean production. We estimate reduced‐form and structural probit models of tillage choice, using a large panel of field‐level soybean management decisions from across the United States spanning 1998–2016. We find that the first emergence of glyphosate‐resistant weed species has little initial effect on tillage practices, though by the time that eight glyphosate‐resistant weed species are identified, conservation tillage and no‐till use fall by 3.9 percentage points and 7.6 percentage points, respectively. We further find that when ten glyphosate‐resistant species are present, the predicted adoption rate of non‐glyphosate herbicides rises 50 percentage points, and that the availability of non‐glyphosate herbicides facilitates continued use of conservation tillage as glyphosate‐resistant weeds proliferate. Using a simple benefits transfer model, we conservatively estimate that between 2008 and 2016 farmers' tillage responses to the spread of glyphosate‐resistant weeds have caused water quality and climate damages via fuel emissions valued at nearly $245 million. This value does not account for climate damages due to carbon released during soil disruptions and is likely to grow as glyphosate resistance becomes more widespread and more farmers turn to tillage for supplemental weed control. [ABSTRACT FROM AUTHOR]

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]