How deregulation, drought and increasing fire impact Amazonian biodiversity.

Biodiversity contributes to the ecological and climatic stability of the Amazon Basin1,2, but is increasingly threatened by deforestation and fire3,4. Here we quantify these impacts over the past two decades using remote-sensing estimates of fire and deforestation and comprehensive range estimates of 11,514 plant species and 3,079 vertebrate species in the Amazon. Deforestation has led to large amounts of habitat loss, and fires further exacerbate this already substantial impact on Amazonian biodiversity. Since 2001, 103,079-189,755 km2 of Amazon rainforest has been impacted by fires, potentially impacting the ranges of 77.3-85.2% of species that are listed as threatened in this region5. The impacts of fire on the ranges of species in Amazonia could be as high as 64%, and greater impacts are typically associated with species that have restricted ranges. We find close associations between forest policy, fire-impacted forest area and their potential impacts on biodiversity. In Brazil, forest policies that were initiated in the mid-2000s corresponded to reduced rates of burning. However, relaxed enforcement of these policies in 2019 has seemingly begun to reverse this trend: approximately 4,253-10,343 km2 of forest has been impacted by fire, leading to some of the most severe potential impacts on biodiversity since 2009. These results highlight the critical role of policy enforcement in the preservation of biodiversity in the Amazon.

Environmental heterogeneity and commodity sharing in smallholder agroecosystems.

Smallholder farmers undertake a number of strategies to cope with climate shocks in a community. The sharing of resources across households constitutes one coping mechanism when environmental shocks differentially impact households. This paper investigates commodity sharing dynamics among households in eight communities in an environmentally heterogeneous highland-lowland area in central Kenya. We use survey data and meteorological data to test whether commodity sharing, measured at the household level by net inflow of commodities, varies across a regional precipitation gradient, and we reveal how sharing fluctuates with rainfall over the course of a year. We find both precipitation and income to be significant predictors of households' net value of shared commodities. Specifically, farmers who live in drier areas with less income are more likely to receive more commodities than they give. We also find that the length of time a household has been established in the area is significantly related to commodity sharing. Further, commodity sharing follows the pattern of harvest and food storage over the course of the year, with households giving the most commodities at times when food storage levels are higher, that is, post-harvest. The study sheds light on the relationship between commodity sharing as a coping mechanism and environmental heterogeneity in a region prone to seasonal food insecurity.

Decentralization can help reduce deforestation when user groups engage with local government.

Policy makers around the world tout decentralization as an effective tool in the governance of natural resources. Despite the popularity of these reforms, there is limited scientific evidence on the environmental effects of decentralization, especially in tropical biomes. This study presents evidence on the institutional conditions under which decentralization is likely to be successful in sustaining forests. We draw on common-pool resource theory to argue that the environmental impact of decentralization hinges on the ability of reforms to engage local forest users in the governance of forests. Using matching techniques, we analyze longitudinal field observations on both social and biophysical characteristics in a large number of local government territories in Bolivia (a country with a decentralized forestry policy) and Peru (a country with a much more centralized forestry policy). We find that territories with a decentralized forest governance structure have more stable forest cover, but only when local forest user groups actively engage with the local government officials. We provide evidence in support of a possible causal process behind these results: When user groups engage with the decentralized units, it creates a more enabling environment for effective local governance of forests, including more local government-led forest governance activities, fora for the resolution of forest-related conflicts, intermunicipal cooperation in the forestry sector, and stronger technical capabilities of the local government staff.

Tree-mycorrhizal associations detected remotely from canopy spectral properties.

A central challenge in global ecology is the identification of key functional processes in ecosystems that scale, but do not require, data for individual species across landscapes. Given that nearly all tree species form symbiotic relationships with one of two types of mycorrhizal fungi - arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) fungi - and that AM- and ECM-dominated forests often have distinct nutrient economies, the detection and mapping of mycorrhizae over large areas could provide valuable insights about fundamental ecosystem processes such as nutrient cycling, species interactions, and overall forest productivity. We explored remotely sensed tree canopy spectral properties to detect underlying mycorrhizal association across a gradient of AM- and ECM-dominated forest plots. Statistical mining of reflectance and reflectance derivatives across moderate/high-resolution Landsat data revealed distinctly unique phenological signals that differentiated AM and ECM associations. This approach was trained and validated against measurements of tree species and mycorrhizal association across ~130 000 trees throughout the temperate United States. We were able to predict 77% of the variation in mycorrhizal association distribution within the forest plots (P < 0.001). The implications for this work move us toward mapping mycorrhizal association globally and advancing our understanding of biogeochemical cycling and other ecosystem processes.

Can incentives make a difference? Assessing the effects of policy tools for encouraging tree-planting on private lands.

This study uses a mail survey of private landowners in the Midwest United States to understand the characteristics of owners who have planted trees or intend to plant trees in the future. The analysis examines what policy tools encourage owners to plant trees, and how policy tools operate across different ownership attributes to promote tree-planting on private lands. Logistic regression results suggest that cost-subsidizing policy tools, such as low-cost and free seedlings, significantly increase the odds of actual and planned reforestation when landowners consider them important for increasing forest cover. Individuals most likely to plant trees, when low-cost seedlings are available and important, are fairly recent (<5 years), college-educated owners who own small parcels (<4 ha) and use the land for recreation. Motivations to reforest were also shaped by owners' planning horizons, connection to the land, previous tree-planting experience, and peer influence. The study has relevance for the design of policy approaches that can encourage private forestation through provision of economic incentives and capacity to private landowners.

Case studies, cross-site comparisons, and the challenge of generalization: comparing agent-based models of land-use change in frontier regions.

Cross-site comparisons of case studies have been identified as an important priority by the land-use science community. From an empirical perspective, such comparisons potentially allow generalizations that may contribute to production of global-scale land-use and land-cover change projections. From a theoretical perspective, such comparisons can inform development of a theory of land-use science by identifying potential hypotheses and supporting or refuting evidence. This paper undertakes a structured comparison of four case studies of land-use change in frontier regions that follow an agent-based modeling approach. Our hypothesis is that each case study represents a particular manifestation of a common process. Given differences in initial conditions among sites and the time at which the process is observed, actual mechanisms and outcomes are anticipated to differ substantially between sites. Our goal is to reveal both commonalities and differences among research sites, model implementations, and ultimately, conclusions derived from the modeling process.

Multi-scale analysis of a household level agent-based model of landcover change.

Scale issues have significant implications for the analysis of social and biophysical processes in complex systems. These same scale implications are likewise considerations for the design and application of models of landcover change. Scale issues have wide-ranging effects from the representativeness of data used to validate models to aggregation errors introduced in the model structure. This paper presents an analysis of how scale issues affect an agent-based model (ABM) of landcover change developed for a research area in the Midwest, USA. The research presented here explores how scale factors affect the design and application of agent-based landcover change models. The ABM is composed of a series of heterogeneous agents who make landuse decisions on a portfolio of cells in a raster-based programming environment. The model is calibrated using measures of fit derived from both spatial composition and spatial pattern metrics from multi-temporal landcover data interpreted from historical aerial photography. A model calibration process is used to find a best-fit set of parameter weights assigned to agents' preferences for different landuses (agriculture, pasture, timber production, and non-harvested forest). Previous research using this model has shown how a heterogeneous set of agents with differing preferences for a portfolio of landuses produces the best fit to landcover changes observed in the study area. The scale dependence of the model is explored by varying the resolution of the input data used to calibrate the model (observed landcover), ancillary datasets that affect land suitability (topography), and the resolution of the model landscape on which agents make decisions. To explore the impact of these scale relationships the model is run with input datasets constructed at the following spatial resolutions: 60, 90, 120, 150, 240, 300 and 480 m. The results show that the distribution of landuse-preference weights differs as a function of scale. In addition, with the gradient descent model fitting method used in this analysis the model was not able to converge to an acceptable fit at the 300 and 480 m spatial resolutions. This is a product of the ratio of the input cell resolution to the average parcel size in the landscape. This paper uses these findings to identify scale considerations in the design, development, validation and application of ABMs of landcover change.