Publications

As climate change makes agricultural production shocks more frequent and severe, it is vital to understand their effect on farmer welfare, land use, and deforestation. Theoretically, a change in agricultural productivity could increase or decrease deforestation by changing demand for agricultural land and/or through the consumption of forests as a coping strategy. This paper uses the introduction of a crop pest to sub-Saharan Africa to estimate the effect of a negative agricultural productivity shock on deforestation. Using primary household data, we first find that farmers who were exposed to higher levels of fall armyworm saw substantial decreases in yield and food security. Using estimates of fall armyworm suitability in conjunction with machine-learning models of maize yields and deforestation, we find that the introduction of the fall armyworm induced a doubling of the deforestation rate in Zambia in the 3 y following the outbreak. This increase was driven both by increased agricultural land expansion and increased charcoal production as a coping strategy. These responses vary substantially over space. More remote areas experienced 23% lower FAW-induced deforestation compared with the sample average, suggesting that farmers with access to maize and charcoal markets may have increased deforestation as a response. Wealthier areas were also less likely to deforest in response to FAW pressure. In sum, our results suggest that negative agricultural productivity shocks may lead to a negative climate feedback, with farmers engaging in emissions-increasing strategies in response.

Over the last decade, attribution science has shown that climate change is responsible for substantial death, disability and illness. However, health impact attribution studies have focused disproportionately on populations in high-income countries, and have mostly quantified the health outcomes of heat and extreme weather. A clearer picture of the global burden of climate change could encourage policymakers to treat the climate crisis like a public health emergency.

Understanding the spawning strategies of large pelagic fish could provide insights into their underlying evolutionary drivers, but large-scale information on spawning remains limited. Here we leverage a near-global larval dataset of 15 large pelagic fish taxa to develop habitat suitability models and use these as a proxy for spawning grounds. Our analysis reveals considerable consistency in spawning in time and space, with 10 taxa spawning in spring/summer and 9 taxa spawning off Northwest Australia. Considering the vast ocean expanse available for spawning, these results suggest that the evolutionary benefits of co-locating spawning in terms of advantageous larval conditions outweigh the benefits of segregated spawning in terms of reduced competition and lower larval predation. Further, tropical species spawn over broad areas throughout the year, whereas more subtropical and temperate species spawn in more restricted areas and seasons. These insights into the spawning strategies of large pelagic fish could inform marine management, including through fisheries measures to protect spawners and through the placement of marine protected areas.

Sustainability requirements imposed on agricultural producers by downstream supply chain actors risk excluding smallholder farmers from upgraded markets. Here we investigated smallholder participation in sustainably certified palm oil mill markets in Indonesia. We developed and applied a conceptual model to estimate the importance of structural market access, smallholder capacity, and buyer/seller behavior in shaping mill smallholder sourcing. Smallholders who hold exclusive contracts with specific mills were overrepresented at certified mills. Independent smallholders unaffiliated with mills contributed one-third of regional oil palm production but 7% of certified mill supply. We found no evidence that independent smallholders exited markets after mill certification (“active” exclusion). Instead, only 36% of certified mills ever purchased from independent smallholders, and independent smallholder lands were less common around certified (29–38% of palm area) versus noncertified (41–42%) mills. To address such “passive” exclusion, supply chain governance programs should encourage participation of actors well-positioned to source from small-scale producers.

Recent public and private policies seek to end deforestation by regulating the production and trade of forest-risk commodities. The design, implementation, and evaluation of these policies rely on metrics that are typically bounded in scope by either territories or supply chains, and therefore only provide a partial account of deforestation on the ground. We argue that metrics linking deforestation and forest degradation to commodity production need to consider two distinct questions: (1) How much of today’s commodity production is associated with past deforestation? and (2) to what extent is today’s deforestation driven by the prospects of producing a specific commodity in the future? This paper describes how metrics can respond to these questions by being classified according to their commodity or deforestation focus. We propose common terminology to facilitate the communication and use of these perspectives and metrics. We then make the case for combining perspectives through the monitoring and reporting of multiple metrics by governments, companies, and non-governmental organizations alike to both assess progress and drive more coordinated action to reduce deforestation.

As climate change increasingly affects biodiversity and ecosystem services, a key challenge in ecology is accurate attribution of these impacts. Though experimental studies have greatly advanced our understanding of climate change effects, experimental results are difficult to generalise to real-world scenarios. To better capture realised impacts, ecologists can use observational data. Disentangling cause and effect using observational data, however, requires careful research design. Here we describe advances in causal inference that can improve climate change attribution in observational settings. Our framework includes five steps: (1) describe the theoretical foundation, (2) choose appropriate observational datasets, (3) estimate the causal relationships of interest, (4) simulate a counterfactual scenario and (5) evaluate results and assumptions using robustness checks. We demonstrate this framework using a pinyon pine case study in North America, and we conclude with a discussion of frontiers in climate change attribution. Our aim is to provide an accessible foundation for applying observational causal inference to estimate climate change effects on ecological systems.

Aquaculture interventions and policies are now fundamental in sustainability agendas, particularly in supporting small-scale fisheries and coastal communities. These policies often rely on the “blue transitions” theory of change, which posits that an expansion of aquaculture will aid in recovering declining fish stocks and enhancing livelihoods. However, the blue transitions theory is relatively new, leaving many aspects uncertain, especially regarding how transition stages unfold and impact communities as they are expected to transform livelihoods. Frequently, these policies adopt a top-down approach driven by political and corporate interests at global or national levels, emphasizing environmental and economic benefits while neglecting local social, cultural, and historical contexts. This study aims to identify gaps in current blue transition policies at the local level through two empirical case studies in Baja California Sur, Mexico. Additionally, it evaluates the suitability of existing frameworks for incorporating justice in food system transitions for seafood system transitions and provides insights for developing more equitable blue food policies. Using an exploratory mixed methods approach from 2021 to 2023, including ethnography, interviews, surveys, and focus groups, this research delves into the complexities of aquaculture policies for communities going through blue transitions. Findings indicate that these policies often prioritize economic development over social, cultural, and historical considerations, leading to injustices within communities. The case studies reveal impacts and challenges such as intra-community conflict, illegal fishing, and threats to food security and resilience, as well as benefits like momentary economic gains. Applying a framework for just food system transitions, we advocate for flexible, community-centric policies that recognize local heterogeneity and empower communities to shape their transitions, including deciding whether a transition is appropriate. This study underscores the limitations of viewing aquaculture as a panacea for small-scale fisheries’ challenges, emphasizing the need for holistic, multiscale management approaches. Contextualizing blue transitions within local realities and prioritizing food justice can promote just and equitable outcomes that address the nuanced needs of diverse coastal communities amidst global pressures.

Climate shocks are increasing, threatening global agricultural production and food security. But a more extreme climate allows for improved predictions and enables advisory services that allow farmers, ranchers and consumers to respond effectively. To date, there is limited uptake of forecasts. How can we make sure these predictions are valued by and valuable for users of agro-climatic forecasts? Over the past two years, we held over 40 interviews with food system stakeholders to identify their needs and shortcomings of existing decision support. In this Commentary, we combine these findings and nascent modeling efforts with existing literature to characterize five lessons for improving the uptake and utilization of predictive tools for last mile users in the agrifood system. Given the explosion of machine learning prediction efforts across many applications, we believe our lessons are broadly applicable to forecasting models intended for decision support. Improved accuracy alone does not necessarily lead to improved decision support, and the trust required to motivate action.

There is a widespread perception that illegal fishing is common in marine protected areas (MPAs) due to strong incentives for poaching and the high cost of monitoring and enforcement. Using artificial intelligence and satellite-based Earth observations, we provide estimates of industrial fishing activity in fully and highly protected MPAs worldwide, in which such fishing is banned. We find little to no activity in most cases. On average, these MPAs had just one fishing vessel present per 20,000 square kilometers during the satellite overpass, a density nine times lower than that of the unprotected waters of exclusive economic zones.

We propose and analyze the application of statistical functional depth metrics for the selection of extreme scenarios for realized electric load, as well as solar and wind generation in day-ahead grid planning. Our primary motivation is screening probabilistic scenarios to identify those most relevant for operational risk mitigation. To handle the high-dimensionality of the scenarios across asset classes and intra-day periods, we employ functional measures of depth to sub-select outlying scenarios that are most likely to be the riskiest for the grid operation. We investigate a range of functional depth measures, as well as a range of operational risks, including load shedding, operational costs, reserve shortfalls, and variable renewable energy curtailment. The effectiveness of the proposed screening approach is demonstrated through a case study on the realistic Texas-7k grid.

Several initiatives to conserve, restore or better manage fisheries, fishes, whales, and other marine animals have been proposed as natural climate solutions to sequester carbon from the atmosphere or avoid new emissions. We reviewed the knowledge and uncertainties surrounding carbon fluxes and storage mediated by these organisms to evaluate their suitability to support climate mitigation interventions. Estimates of the carbon stored within fish and marine mammal biomass ranged from 0.1 to 1.9 Pg C for mesopelagic fishes, 0.7–0.6 Pg C for all fishes, 0.0020–0.016 Pg C for great whales, and 0.0065–0.0113 Pg C for all marine mammals, compared to an estimated 1.5–6 Pg C stored in all ocean biota. Mesopelagic fishes, epipelagic fishes and great whales contribute on the order of 1–3 Pg C yr−1, 0.03–0.06 Pg C yr−1, and 0.001–0.004 Pg C yr−1, respectively, to export from the ocean's surface below the euphotic zone, compared to an estimated total marine biological export of 9–10 Pg C yr−1. The combined flux of carbon to the atmosphere from benthic trawling, biomass extraction, and fuel consumption during commercial fishing ranged from 0.05 to 0.25 Pg C yr−1. Substantial uncertainties were associated with nearly all fluxes and reservoirs. The contributions of whales to carbon export and the mobilization of sediment carbon during benthic trawling were least certain, limiting the readiness of associated pathways to provide quantifiable, high-quality carbon credits. Although substantial uncertainties also surrounded mesopelagic fishes, we found that even conservative estimates of these organisms' contribution to ocean carbon export are large enough to justify conservation actions.

Climate change threatens global food systems1, but the extent to which adaptation will reduce losses remains unknown and controversial2. Even within the well-studied context of US agriculture, some analyses argue that adaptation will be widespread and climate damages small3,4, whereas others conclude that adaptation will be limited and losses severe5,6. Scenario-based analyses indicate that adaptation should have notable consequences on global agricultural productivity7,8,9, but there has been no systematic study of how extensively real-world producers actually adapt at the global scale. Here we empirically estimate the impact of global producer adaptations using longitudinal data on six staple crops spanning 12,658 regions, capturing two-thirds of global crop calories. We estimate that global production declines 5.5 × 1014 kcal annually per 1 °C global mean surface temperature (GMST) rise (120 kcal per person per day or 4.4% of recommended consumption per 1 °C; P < 0.001). We project that adaptation and income growth alleviate 23% of global losses in 2050 and 34% at the end of the century (6% and 12%, respectively; moderate-emissions scenario), but substantial residual losses remain for all staples except rice. In contrast to analyses of other outcomes that project the greatest damages to the global poor10,11, we find that global impacts are dominated by losses to modern-day breadbaskets with favourable climates and limited present adaptation, although losses in low-income regions losses are also substantial. These results indicate a scale of innovation, cropland expansion or further adaptation that might be necessary to ensure food security in a changing climate.

Agricultural land abandonment presents potential environmental benefits through either revegetation, and associated carbon sequestration, habitat and landscape connectivity benefits, or recultivation, which offsets the need for agricultural conversion of natural areas. Yet, the extent and pace of land abandonment depends on future demand for food, energy, and other anthropogenic drivers. Here we quantify the extent and spatial distribution of agricultural abandonment and conversion in the southeastern US under a range of future development scenarios, addressing (1) what is the extent of future agricultural abandonment and conversion, (2) how much forecast agricultural conversion could be offset by recultivation of abandoned land, and (3) within a given development scenario, how do different strategies for recultivation of abandoned lands influence (a) habitat fragmentation and (b) connectivity for the umbrella species Ursus americanus. Future abandonment ranged from 1.63 Mha (local economic scenario) to 7.95 Mha (local environmental scenario). Future conversion ranged from 1.24 Mha (global environmental scenario) to 5.65 Mha (global economic scenario). While environmental scenarios predicted surplus abandonment available to offset all conversion, economic scenarios predicted enough abandonment to offset a third of conversion at most. Within a given development scenario, strategic recultivation targeting carbon or biodiversity conservation can reduce fragmentation by up to 17% compared to land-use decisions that do not consider those characteristics. However, strategic recultivation did not significantly affect connectivity, which was instead driven by development scenarios: cost-weighted distance to least-cost path ratio was highest in the economic development scenarios and lowest in the environmental concern scenarios. Our results suggest that while socio-economic development scenarios are the primary drivers of land-use change patterns and the attendant ecological consequences, strategic recultivation decisions targeting carbon sequestration or biodiversity potential can reduce habitat fragmentation within development scenarios.

Forests in the Western United States face escalating threats from wildfire, pest outbreaks, and drought, leading experts and policymakers to call for extensive forest management to promote forest resilience and reduce wildfire risk. High treatment costs represent a major choke point to achieving forest management goals, but selling timber and biomass from forest thinning can offset costs and provide the revenue to help rapidly scale management actions. In this study, we assess how forest product market conditions influence the economic feasibility and scale of forest management by modeling treatment potential across a Northern Sierra Nevada landscape using the US Forest Service's BioSum tool. We evaluate treatment outcomes across nine economic scenarios, incorporating wood and biomass price variations and treatment subsidies. Results indicate that baseline pricing assumptions facilitate aggressive forest treatment where thinning is feasible and needed, but a 50 % drop in forest product prices led to a one-third decline in treated area. These reductions are completely offset by a $500/acre treatment subsidy, suggesting that subsidies could serve as a ‘price floor’ to maintain treatment levels through market fluctuations. Optimal, cost-effective treatments overwhelmingly utilized prescribed fire following thinning, emphasizing the role of fire-inclusive approaches for forest treatment. While study findings indicate that forest product markets can support landscape-scale treatments, the capacity of regional processing facilities currently limits full utilization of forest products, underscoring the importance of expanding wood and biomass utilization infrastructure to realize the potential of market-driven strategies for improving forest resilience in the Sierra Nevada and similar fire-prone regions.
 

California has some of the highest electricity prices in the country. These prices — which are too high even accounting for climate and pollution damages (i.e.,
social marginal cost) — make it harder for people to afford their energy bills and discourage the switch to cleaner electric options like electric cars, heat pumps,
and other appliances. California’s greenhouse gas (GHG) cap-and-trade (C&T) program has been integral to the state achieving its climate goals by inducing cost-effective (and thus affordable) emissions reductions. It has also generated over $50 billion in permit auction revenues since 2013, which can be used to tackle energy affordability through funding direct reductions in retail electricity prices. We explored how reallocating the $1.2 billion in C&T funding from the residential California Climate Credit program to directly subsidizing retail electricity rates could lower prices for PG&E, SCE, and SDG&E customers. Ultimately, California’s GHG C&T program tackles energy affordability both by offering the most cost effective way to meet climate goals and by generating
revenue to help reduce high electricity prices.

Many behavioral responses to climate change are carbon-intensive, raising concerns that adaptation may cause additional warming. The sign and magnitude of this feedback depend on how increased emissions from cooling balance against reduced emissions from heating across space and time. We present an empirical approach that forecasts the effect of future adaptive energy use on global average temperature over the 21st century. We estimate that energy-based adaptation will lower global mean surface temperature in 2099 by 0.07 to 0.12 °C relative to baseline projections under Representative Concentration Pathways 4.5 and 8.5. This cooling avoids 0.6 to 1.8 trillion U.S. Dollars ($2019) in damages, depending on the baseline emissions scenario. Energy-based adaptation lowers business-as-usual emissions for 85% of countries, reducing the mitigation required to meet their unilateral Nationally Determined Contributions by 20% on average. These findings indicate that while business-as-usual adaptive energy use is unlikely to accelerate warming, it raises important implications for countries’ existing mitigation commitments.

Anticipating precipitation (PPT) extremes across sub-Saharan Africa can help mobilize interventions, trigger anticipatory actions, and promote beneficial actions like water harvesting. Reliable crop model forecasts can help identify when and where food aid interventions can be most beneficial. To date, however, there has been little research evaluating the utility of rainfall forecasts. This study, therefore, assesses the efficacy of the Subseasonal Consortium database (SubC) for use in a regional crop water balance model—the water requirement satisfaction index (WRSI)—in east Africa. We find that combining two dekads (20 d) of statistically downscaled and bias-corrected SubC PPT data with climatological information delivers improved estimates of end-of-season conditions over a 17 year test period. Our results show that SubC forecasts provide a 35%–55% reduction in EOS WRSI root mean squared error in 60% of the east African agropastoral areas during the short rains, with the highest accuracy being in areas that are most vulnerable to inconsistent PPT timing and quantities. Across the 17 tested seasons, 1999/00–2015/16, use of the SubC either improved or did not degrade the accuracy of WRSI prediction compared to a benchmark model for over 70% of the seasons and for 90% of the study region. In general, the improved accuracy provided by two dekads of SubC forecast is nearly equivalent to what can be attained with one dekad of a ‘perfect’ forecast (i.e. observation data). In effect, a 20 day forecast provides a 10 day advance in our early warning capabilities. During extreme events, such as during the 2005/2006 drought in east Africa, the SubC-driven WRSI could provide advanced warning of poor cropping conditions and potential crop failure up to 3 months before the end of the season. Overall, these improvements provide earlier and more accurate estimates of the likely seasonal water balance outcomes, and allow for the identification of locations where interventions may be needed.

We propose and analyze the application of statistical functional depth metrics for the selection of extreme scenarios for realized electric load, as well as solar and wind generation in day-ahead grid planning. Our primary motivation is screening probabilistic scenarios to identify those most relevant for operational risk mitigation. To handle the high-dimensionality of the scenarios across asset classes and intra-day periods, we employ functional measures of depth to sub-select outlying scenarios that are most likely to be the riskiest for the grid operation. We investigate a range of functional depth measures, as well as a range of operational risks, including load shedding, operational costs, reserve shortfalls, and variable renewable energy curtailment. The effectiveness of the proposed screening approach is demonstrated through a case study on the realistic Texas-7k grid.

We examine whether the mobility of a transboundary stock can incentivize a bilateral marine reserve agreement, where one fishing country pays its neighbor to become a marine reserve. The key insight is that precisely because fish move across borders, non-cooperative extraction is likely to be excessive, which lowers fishery profits for both countries. Under our proposal, the payor benefits from the spillover and elimination of non-cooperative extraction, both of which raise fishing profits. The payee benefits from direct financial compensation in exchange for creating the marine reserve. We create a dynamic and spatial game between two countries sharing a transboundary stock. We determine the mobility conditions under which the payor is willing to pay and the payee is willing to accept the payment. We illuminate the biological and economic conditions under which an agreement is incentive-compatible for both countries and conditions under which one or both countries would oppose such an agreement. We find that larval dispersal plays little role in agreement acceptance if adult movement is high. However, if adult movement is low, high larval dispersal can promote agreement acceptance. We also find that this agreement can fully reproduce the conservation and economic benefits gained under a cooperative fishing agreement if adult movement from the fishing patch to the reserve patch is low (≤ 40%) and adult movement to the fishing patch is high (≥ 60%). We discuss the implementation challenges using four real-world transboundary stocks and highlight global issues that bilateral marine reserve agreements can help solve.