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Wildfires are a growing concern to society and the environment in many parts of the world. Within the United States, the land area burned by wildfires has steadily increased over the past 40 years. Agricultural land management is widely understood as a force that alters fire regimes, but less is known about how wildfires, in turn, impact the agriculture sector. Based on an extensive literature review, we identify three pathways of impact-direct, downwind and downstream-through which wildfires influence agricultural resources (soil, water, air and photosynthetically active radiation), labour (agricultural workers) and products (crops and livestock). Through our pathways framework, we highlight the complexity of wildfire-agriculture interactions and the need for collaborative, systems-oriented research to better quantify the magnitude of wildfire impacts and inform the adaptation of agricultural systems to an increasingly fire-prone future., (© 2023. Springer Nature Limited.)

Development and implementation of effective protected area management to reduce deforestation depend in part on identifying factors contributing to forest loss and areas at risk of conversion, but standard land-use-change modeling may not fully capture contextual factors that are not easily quantified. To better understand deforestation and agricultural expansion in Amazonian protected areas, we combined quantitative land-use-change modeling with qualitative discourse analysis in a case study of Brazil's Jamanxim National Forest. We modeled land-use change from 2008 to 2018 and projected deforestation through 2028. We used variables identified in a review of studies that modeled land-use change in the Amazon (e.g., variables related to agricultural suitability and economic accessibility) and from a critical discourse analysis that examined documents produced by different actors (e.g., government agencies and conservation nonprofit organizations) at various spatial scales. As measured by analysis of variance, McFadden's adjusted pseudo R 2 , and quantity and allocation disagreement, we found that including variables in the model identified as important to deforestation dynamics through the qualitative discourse analysis (e.g., the proportion of unallocated public land, distance to proposed infrastructure developments, and density of recent fires) alongside more traditional variables (e.g., elevation, distance to roads, and protection status) improved the predictive ability of these models. Models that included discourse analysis variables and traditional variables explained up to 19.3% more of the observed variation in deforestation probability than a model that included only traditional variables and 4.1% more variation than a model with only discourse analysis variables. Our approach of integrating qualitative and quantitative methods in land-use-change modeling provides a framework for future interdisciplinary work in land-use change., (© 2022 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)

Studying land use change in protected areas (PAs) located in tropical forests is a major conservation priority due to high conservation value (e.g., species richness and carbon storage) here, coupled with generally high deforestation rates. Land use change researchers use a variety of land cover products to track deforestation trends, including maps they produce themselves and readily available products, such as the Global Forest Change (GFC) dataset. However, all land cover maps should be critically assessed for limitations and biases to accurately communicate and interpret results. In this study, we assess deforestation in PA complexes located in agricultural frontiers in the Amazon Basin. We studied three specific sites: Amboró and Carrasco National Parks in Bolivia, Jamanxim National Forest in Brazil, and Tambopata National Reserve and Bahuaja-Sonene National Park in Peru. Within and in 20km buffer areas around each complex, we generated land cover maps using composites of Landsat imagery and supervised classification, and compared deforestation trends to data from the GFC dataset. We then performed a dissimilarity analysis to explore the discrepancies between the two remote sensing products. Both the GFC and our supervised classification showed that deforestation rates were higher in the 20km buffer than inside the PAs and that Jamanxim National Forest had the highest deforestation rate of the PAs we studied. However, GFC maps showed consistently higher rates of deforestation than our maps. Through a dissimilarity analysis, we found that many of the inconsistencies between these datasets arise from different treatment of mixed pixels or different parameters in map creation (for example, GFC does not detect reforestation after 2012). We found that our maps underestimated deforestation while GFC overestimated deforestation, and that true deforestation rates likely fall between our two estimates. We encourage users to consider limitations and biases when using or interpreting our maps, which we make publicly available, and GFC's maps., Competing Interests: The authors have declared that no competing interests exist.

Addressing how ecosystem services (ES) are distributed among groups of people is critical for making conservation and environmental policy-making more equitable. Here, we evaluate the distribution and equity of changes in ES benefits across demographic and socioeconomic groups in the United States (US) between 2020 and 2100. Specifically, we use land cover and population projections to model potential shifts in the supply, demand, and benefits of the following ES: provision of clean air, protection against a vector-borne disease (West Nile virus), and crop pollination. Across the US, changes in ES benefits are unevenly distributed among socioeconomic and demographic groups and among rural and urban communities, but are relatively uniform across geographic regions. In general, non-white, lower-income, and urban populations disproportionately bear the burden of declines in ES benefits. This is largely driven by the conversion of forests and wetlands to cropland and urban land cover in counties where these populations are expected to grow. In these locations, targeted land use policy interventions are required to avoid exacerbating inequalities already present in the US.

Combining qualitative and quantitative methods and data is crucial to understanding the complex dynamics and often interdisciplinary nature of conservation. Many conservation scientists use mixed methods, but there are a variety of mixed methods approaches, a lack of shared vocabulary, and few methodological frameworks. We reviewed articles from 2 conservation-related fields that often incorporate qualitative and quantitative methods: land-change science (n = 16) and environmental management (n = 16). We examined how authors of these studies approached mixed methods research by coding key methodological characteristics, including relationships between method objectives, extent of integration, iterative interactions between methods, and justification for use of mixed methods. Using these characteristics, we created a typology with the goal of improving understanding of how researchers studying land-change science and environmental management approach interdisciplinary mixed methods research. We identified 5 types of mixed methods approaches, which we termed simple nested, informed nested, simple parallel, unidirectional synthesis, and bidirectional synthesis. Methods and data sources were often used to address different research questions within a project, and only around half of the reviewed papers methodologically integrated different forms of data. Most authors used one method to inform the other, rather than both informing one another. Very few articles used methodological iteration. Each methodological type has certain epistemological implications, such as the disciplinary reach of the research and the capacity for knowledge creation through the exchange of information between distinct methodologies. To exemplify a research design that can lead to multidimensional knowledge production, we provide a methodological framework that bidirectionally integrates and iterates qualitative and quantitative methods., (© 2020 Society for Conservation Biology.)