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.

Rosario, Argentina, a city of more than one million people strategically located on the Paraná River in the heart of a fertile agricultural region, is home to a significant industrial corridor where ongoing urbanization for industry, including that associated with the port complex and agroexport industries, vies for real estate space with peri-urban and urban farming production. The city is also the site of thriving municipal programs seeking to change food production and consumption outcomes through urban and peri-urban agriculture projects rooted in agroecology. This paper identifies the socio-natures critical for the formation and endurance of these agroecology assemblages. Based on interviews with 30 stakeholders in government, civil society, and agricultural production, we describe the integrated approach to environmental, social, and economic sustainability embedded in Rosario's institutional agroecology programs. In particular, we discuss the actors and strategies (which seek to preserve land for agricultural uses), discursive renderings of socio-natures (as valuable biodiverse territories and productive diverse bodies), and the marketing of agroecological materialities (through production for public markets) that form and are formed by these assemblages. We also discuss the power dynamics embedded in sustaining urban and peri-urban agroecological projects through institutional means. This research contributes to literature on agroecology, urban agriculture, and the urban metabolism through providing empirical examples of socio-natural entanglements in urban agroecological assemblages.

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.

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 R2, 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.