Clark, M. C. | Search bioRxiv

bioRxiv2023

Quantifying Local Perceptions of Environmental Change and Links to Community-Based Conservation Practices

Approximately two billion people -- a quarter of the earths population -- directly harvest forest products to meet their daily needs. These individuals disproportionately experience the impacts of increasing climatic variability and global biodiversity loss, and must disproportionately alter their behaviors in response to these impacts. Much of the increasingly ambitious global conservation agenda relies on voluntary uptake of conservation behaviors in such populations. Thus, it is critical to understand how individuals in these communities perceive environmental change and use conservation practices as a tool to protect their well-being. To date however, there have been no quantitative studies of how individual perceptions of forest change and its causes shape real-world conservation behaviors in forest dependent populations. Here we use a novel participatory mapping activity to elicit spatially explicit perceptions of forest change and its drivers across 43 mangrove-dependent communities in Pemba, Tanzania. We show that perceptions of mangrove decline drive individuals to propose stricter limits on fuelwood harvests from community forests only if they believe that the resultant gains in mangrove cover will not be stolen by outsiders. Conversely, individuals who believe their community mangrove forests are at high risk of theft actually decrease their support for forest conservation in response to perceived forest decline. High rates of inter-group competition and mangrove loss are thus driving a race to the bottom phenomenon in community forests in this system. This finding demonstrates a mechanism by which increasing environmental decline may cause communities to forgo conservation practices, rather than adopt them, as is often assumed in much community-based conservation planning. However, we also show that when effective boundaries are present, individuals are willing to limit their own harvests to stem such perceived decline.

ecology↗

bioRxiv2023

Causal attribution of agricultural expansion in a small island system using approximate Bayesian computation

The extent and arrangement of land cover types on our planet directly affects biodiversity, carbon storage, water quality, and many other critical social and ecological conditions at virtually all scales. Given the fundamental importance of land cover, a key mandate for land system scientists is to describe the mechanisms by which pertinent cover types spread and shrink. Identifying causal drivers of change is challenging however, because land systems, such as small-scale agricultural communities, do not lend themselves well to controlled experimentation for logistical and ethical reasons. Even natural experiments in these systems can produce only limited causal inference as they often contain unobserved confounding drivers of land cover change and complex feedbacks between drivers and outcomes. Land system scientists commonly grapple with this complexity by using computer simulations to explicitly delineate hypothesized causal pathways that could have resulted in observed land cover change. Yet, land system science lacks a systematic method for comparing multiple hypothesized pathways and quantifying the probability that a given simulated causal process was in fact responsible for the patterns observed. Here we use a case study of agricultural expansion in Pemba, Tanzania to demonstrate how approximate Bayesian computation (ABC) provides a straightforward solution to this methodological gap. Specifically, we pair an individual-based simulation of land cover change in Pemba with ABC to probabilistically estimate the likelihood that observed deforestation from 2018 to 2021 was driven by soil degradation rather than external market forces. Using this approach, we can show not only how well a specific hypothesized mechanism fits with empirical data on land cover change, but we can also quantify the range of other mechanisms that could have reasonably produced the same outcome (i.e. equifinality). While ABC was developed for use in population genetics, we argue that it is particularly promising as a tool for causal inference for land system science given the wealth of data available in the satellite record. Thus, this paper demonstrates a robust process for identifying the emergent landscape-level signatures of complex social-ecological mechanisms.

ecology↗