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Non-crop vegetation, such as hedgerows and cover crops, are important on-farm diversification practices that support biodiversity and ecosystem services; however, information about their rates and patterns of adoption are scarce. We used satellite and aerial imagery coupled with machine learning classification to map the use of hedgerows/windbreaks and winter cover crops in California's Central Coast, a globally important agricultural area of intensive fresh produce production. We expected that adoption of both practices would be relatively low and unevenly distributed across the landscape, with higher levels of adoption found in marginal farmland and in less intensively cultivated areas where the pressure to remove non-crop vegetation may be lower. Our remote sensing classification revealed that only ~6% of farmland had winter cover crops in 2021 and 0.26% of farmland had hedgerows or windbreaks in 2018. Thirty-seven percent of ranch parcels had cover crops on at least 5% of the ranch while 22% of ranches had at least one hedgerow/windbreak. Nearly 16% of farmland had other annual winter crops, some of which could provide services similar to cover crops; however, 60% of farmland had bare soil over the winter study period, with the remainder of farmland classified as perennial crops or strawberries. Hotspot analysis showed significant areas of adoption of both practices in the hillier regions of all counties. Finally, qualitative interviews revealed that adoption patterns were likely driven by interrelated effects of topography, land values, and farming models, with organic, diversified farms implementing these practices in less ideal, lower-value farmland. This study demonstrates how remote sensing coupled with qualitative research can be used to map and interpret patterns of important diversification practices, with implications for tracking policy interventions and targeting resources to assist farmers motivated to expand adoption. 

As climate change increases the frequency and severity of droughts in many regions, conservation during drought is becoming a major challenge for ecologists. Droughts are multidimensional climate events whose impacts may be moderated by changes in temperature, water availability or food availability, or some combination of these. Simultaneously, other stressors such as extensive anthropogenic landscape modification may synergize with drought. Useful observational models for guiding conservation decision‐making during drought require multidimensional, dynamic representations to disentangle possible drought impacts, and consequently, they will require large, highly resolved data sets. In this paper, we develop a two‐stage predictive framework for assessing how drought impacts vary with species, habitats and climate pathways.

Central Valley, California, USA.

We used a two‐stage counterfactual analysis combining predictive linear mixed models and N‐mixture models to characterize the multidimensional impacts of drought on 66 bird species. We analysed counts from the eBird participatory science data set between 2010 and 2019 and produced species‐ and habitat‐specific estimates of the impact of drought on relative abundance.

We found that while fewer than a quarter (16/66) of species experienced abundance declines during drought, nearly half of all species (27/66) changed their habitat associations during drought. Among species that shifted their habitat associations, the use of natural habitats declined during drought while use of developed habitat and perennial agricultural habitat increased.

Our findings suggest that birds take advantage of agricultural and developed land with artificial irrigation and heat‐buffering microhabitat structure, such as in orchards or parks, to buffer drought impacts. A working lands approach that promotes biodiversity and mitigates stressors across a human‐induced water gradient will be critical for conserving birds during drought.

 

In the past few decades, farmers and researchers have firmly established that biologically diversified farming systems improve ecosystem services both on and off the farm, producing economic benefits for farmers and ecological benefits for surrounding landscapes. However, adoption of these practices has been slow, requiring a more nuanced examination of both barriers and opportunities to improve adoption rates. While previous research has demonstrated that both individual and structural factors shape farmers' decisions about whether to adopt diversification practices, this study aims to understand the interaction of these individual and structural factors, and how they relate to farm scale. Based on 20 interviews with organic lettuce growers on the Central Coast of California, as well as 8 interviews with technical assistance providers who work with these growers, we constructed a typology to help elucidate the distinct contexts that shape growers' decisions about diversification practices. This typology, which reflects the structural influence of land rent and supply chains, divides growers into three categories: limited resource, mid-scale diversified, or wholesale. In this economic context, limited resource and wholesale growers both experience significant barriers that constrain the adoption of diversification practices, while some mid-scale diversified growers have found a “sweet spot” for managing agroecosystems that can succeed in both economic and ecological terms. The key enabling factors that allow these farmers to choose diversification, however, are not directly related to their farm size, but have more to do with secure land tenure, adequate access to capital and resources, and buyers who share their values and are willing to pay a premium. By focusing on these key enabling factors with targeted policies, we believe it is possible to encourage diversification practices on farms at a variety of scales within California's Central Coast. 

Understanding how and why animals use the environments where they occur is both foundational to behavioral ecology and essential to identify critical habitats for species conservation. However, some behaviors are more difficult to observe than others, which can bias analyses of raw observational data. To our knowledge, no method currently exists to model how animals use different environments while accounting for imperfect behavior‐specific detection probability. We developed an extension of a binomial N‐mixture model (hereafter the behavior N‐mixture model) to estimate the probability of a given behavior occurring in a particular environment while accounting for imperfect detection. We then conducted a simulation to validate the model's ability to estimate the effects of environmental covariates on the probabilities of individuals performing different behaviors. We compared our model to a naïve model that does not account for imperfect detection, as well as a traditional N‐mixture model. Finally, we applied the model to a bird observation data set in northwest Costa Rica to quantify how three species behave in forests and farms. Simulations and sensitivity analyses demonstrated that the behavior N‐mixture model produced unbiased estimates of behaviors and their relationships with predictor variables (e.g., forest cover, habitat type). Importantly, the behavior N‐mixture model accurately characterized uncertainty, unlike the naïve model, which often suggested erroneous effects of covariates on behaviors. When applied to field data, the behavior N‐mixture model suggested that Hoffmann's woodpecker (Melanerpes hoffmanii) and Inca dove (Columbina inca) behaved differently in forested versus agricultural habitats, while turquoise‐browed motmot (Eumomota superciliosa) did not. Thus, the behavior N‐mixture model can help identify habitats that are essential to a species' life cycle (e.g., where individuals nest, forage) that nonbehavioral models would miss. Our model can greatly improve the appropriate use of behavioral survey data and conclusions drawn from them. In doing so, it provides a valuable path forward for assessing the conservation value of alternative habitat types.

 

Diversifying agricultural landscapes may mitigate biodiversity declines and improve pest management. Yet landscapes are rarely managed to suppress pests, in part because researchers seldom measure key variables related to pest outbreaks and insecticides that drive management decisions. We used a 13‐year government database to analyse landscape effects on European grapevine moth (Lobesia botrana) outbreaks and insecticides acrossc. 400 Spanish vineyards. At harvest, we found pest outbreaks increased four‐fold in simplified, vineyard‐dominated landscapes compared to complex landscapes in which vineyards are surrounded by semi‐natural habitats. Similarly, insecticide applications doubled in vineyard‐dominated landscapes but declined in vineyards surrounded by shrubland. Importantly, pest population stochasticity would have masked these large effects if numbers of study sites and years were reduced to typical levels in landscape pest‐control studies. Our results suggest increasing landscape complexity may mitigate pest populations and insecticide applications. Habitat conservation represents an economically and environmentally sound approach for achieving sustainable grape production.