While conservation goals have long been pursued through traditional species-augmenting actions, a broader set of episodic ecosystem modification (EEM) actions, such as hydropower dam releases, prescribed fire, and beach nourishment, is garnering attention. EEM actions face several implementation challenges stemming from high opportunity costs, delayed effect mechanisms, reliance on monitoring for deployment timing, and outcome uncertainty due to infrequent use. In this paper, we study the use of EEM actions in the form of designer flows—ecologically-motivated releases of water into regulated river segments—to maintain a viable population of a threatened native fish species in the Colorado River. We demonstrate how the cost-effectiveness of EEM actions can be hampered by the complex and delayed effects on species viability, but enhanced through targeted monitoring for timing deployment and experimentation for reducing uncertainty about effectiveness.

The spread of non-native freshwater mussels in North America is a growing threat that has already resulted in substantial ecological and economic damage to infested areas. A primary vector by which invasive mussels spread is watercraft that are transported over land from an infested waterbody to an uninfested waterbody. Management efforts such as watercraft inspection and detection programs that attempt to intercept infected watercraft can help limit this type of spread, but optimizing the effectiveness of these programs under limited resources is complicated. Studies have looked at developing watercraft inspection and decontamination program strategies that focus on policy-specific objectives such as maximizing the number of infested watercraft interceptions. However, there is limited work that has considered the heterogeneous impact of protection efforts across different regions and waterbodies. Knowledge about this heterogeneity can highlight regions that would benefit the most from protection as well as regions that would require less effort to protect, e.g., areas with naturally unsuitable water quality for dreissenids. To this end, we construct a composite relative risk index (CRR) for watersheds within the Missouri River Basin, a region in the United States on the front line of dreissenid spread. The CRR uses a model that mirrors an expected value model but uses relative indexing as a proxy for the model components. The CRR incorporates a wide array of data sets to account for the direct and indirect damages from a potential infestation along with the risk of an infestation occurring. Our results suggest that the relative priority of a specific watershed—measured through CRR—can depend on whether we consider the entire Missouri River Basin or just the watersheds in the same state. This also indicates substantial state-level heterogeneity in the CRR. Another contribution is that the CRR index includes user-specified weights for certain parameters so that a user can adjust the relative importance of various factors to match their specific context. An accompanying web tool allows users to view the CRR results and adjust multiple parameters to see the resulting impacts on the CCR for watersheds in the Missouri River Basin.

Ecosystem-based fisheries management (EBFM) has emerged as a promising framework for understanding and managing the long-term interactions between fisheries and the larger marine ecosystems in which they are nested. However, successful implementation of EBFM has been elusive because we still lack a comprehensive understanding of the network of interacting species in marine ecosystems (the food web) and the dynamic relationship between the food web and the humans who harvest those ecosystems. Here, we advance such understanding by developing a network framework that integrates the complexity of food webs with the economic dynamics of different management policies. Specifically, we generate hundreds of different food web models with 20-30 species, each harvested by five different fishers extracting the biomass of a target and a bycatch species, subject to two different management scenarios and exhibiting different information in terms of avoiding bycatch when harvesting the target species. We assess the different ecological and economic consequences of these policy alternatives as species extinctions and profit from sustaining the fishery. We present the results of different policies relative to a benchmark open access scenario where there are no management policies in place. The framework of our network model would allow policymakers to evaluate different management approaches without compromising on the ecological complexities of a fishery.This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Empirical research involves multiple, seemingly-minor choices that can substantially impact a study's findings. While acknowledged, the importance of these "degrees of flexibility" on published estimates is not well understood. We examine the considerable literature focused on the impacts of early COVID-19 policies on social distancing to assess the role of researchers' degrees of flexibility on the estimated effects of mobility-reducing policies. We find that estimates reported in previous studies are not robust to minor changes in typically-unexplored dimensions of the degree of flexibility space, and usual robustness tests systematically fail to detect these issues.

International trade continues to drive biological invasions. We investigate the drivers of global nonnative ant establishments over the last two centuries using a Cox proportional hazards model. We use country-level discovery records for 36 of the most widespread nonnative ant species worldwide from 1827 to 2012. We find that climatic similarity combined with cumulative imports during the 20 years before a species discovery in any given year is an important predictor of establishment. Accounting for invasions from both the native and previously invaded "bridgehead" regions substantially improves the model's fit, highlighting the role of spatial spillovers. These results are valuable for targeting biosecurity efforts.

Abstract Managing social‐ecological systems (SES) requires balancing the need to tailor actions to local heterogeneity and the need to work over large areas to accommodate the extent of SES. This balance is particularly challenging for policy since the level of government where the policy is being developed determines the extent and resolution of action. We make the case for a new research agenda focused on ecological federalism that seeks to address this challenge by capitalizing on the flexibility afforded by a federalist system of governance. Ecological federalism synthesizes the environmental federalism literature from law and economics with relevant ecological and biological literature to address a fundamental question: What aspects of SES should be managed by federal governments and which should be allocated to decentralized state governments? This new research agenda considers the bio‐geo‐physical processes that characterize state‐federal management tradeoffs for biodiversity conservation, resource management, infectious disease prevention, and invasive species control. Read the free Plain Language Summary for this article on the Journal blog.

The losses from extinction events are not well-known, making an expected net benefits approach to conservation problems difficult to implement. A viable control strategy instead focuses on limiting the risk of extinction to some acceptably low level at the least possible cost. Here we describe a shadow value viability approach for solving conservation problems with irreversible thresholds with dynamic programming. A social planner calculates the minimal (virtual) level of loss from extinction that would trigger sufficient action to avoid extinction with the desired confidence. The cost-effective policy then arises from acting as if the resulting shadow value is real. We demonstrate the method in a numerical application to the conservation of the Pacific leatherback turtle population, which co-mingles with the Pacific swordfish fishery. We show how the cost-effective outcome can be achieved among decentralized fishers by using the planner's shadow value to set market-based instruments for managing turtle bycatch. This approach translates the species viability objective into economic terms so conservation and commercial harvest can be rationally integrated.

Biodiversity in ecosystems plays an important role in supporting human welfare, including regulating the transmission of infectious diseases. Many of these services are not fully-appreciated due to complex environmental dynamics and lack of baseline data. Multicontinental amphibian decline due to the fungal pathogen Batrachochytrium dendrobatidis (Bd) provides a stark example. Even though amphibians are known to affect natural food webs—including mosquitoes that transmit human diseases—the human health impacts connected to their massive decline have received little attention. Here we leverage a unique ensemble of ecological surveys, satellite data, and newly digitized public health records to show an empirical link between a wave of Bd-driven collapse of amphibians in Costa Rica and Panama and increased human malaria incidence. Subsequent to the estimated date of Bd-driven amphibian decline in each ‘county’ (canton or distrito), we find that malaria cases are significantly elevated for several years. For the six year peak of the estimated effect, the annual expected county-level increase in malaria ranges from 0.76 to 1.1 additional cases per 1000 population. This is a substantial increase given that cases country-wide per 1000 population peaked during the timeframe of our study at approximately 1.5 for Costa Rica and 1.1 for Panama. This previously unidentified impact of biodiversity loss illustrates the often hidden human welfare costs of conservation failures. These findings also show the importance of mitigating international trade-driven spread of similar emergent pathogens like Batrachochytrium salamandrivorans .

The COVID-19 pandemic brought unprecedented policy responses and a large literature evaluating their impacts.This paper re-examines this literature and investigates the role of researchers' degrees-of-flexibility on the estimated effects of mobility-reducing policies on socialdistancing behavior.We find that two-way fixed effects estimates are not robust to minor changes in usually-unexplored dimensions of the degree-of-flexibility space.While standard robustness tests based on the sequential addition of covariates are very stable, small changes in the outcome variable and its transformation lead to large and sometimes contradictory changes in the estimates, where the same policy can be found to significantly increase or decrease mobility.Yet, due to the large number of degrees-of-flexibility, one can focus on a set of results that appears stable, while ignoring problematic ones.We show that recently developed heterogeneity-robust difference-in-differences estimators only partially mitigate these issues, and discuss how a strategy of identifying the point at which a sequence of ever more-stringent robustness tests eventually fail could increase the credibility of policy evaluations.

COVID-19 vaccines have been authorized in multiple countries, and more are under rapid development. Careful design of a vaccine prioritization strategy across sociodemographic groups is a crucial public policy challenge given that 1) vaccine supply will be constrained for the first several months of the vaccination campaign, 2) there are stark differences in transmission and severity of impacts from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across groups, and 3) SARS-CoV-2 differs markedly from previous pandemic viruses. We assess the optimal allocation of a limited vaccine supply in the United States across groups differentiated by age and essential worker status, which constrains opportunities for social distancing. We model transmission dynamics using a compartmental model parameterized to capture current understanding of the epidemiological characteristics of COVID-19, including key sources of group heterogeneity (susceptibility, severity, and contact rates). We investigate three alternative policy objectives (minimizing infections, years of life lost, or deaths) and model a dynamic strategy that evolves with the population epidemiological status. We find that this temporal flexibility contributes substantially to public health goals. Older essential workers are typically targeted first. However, depending on the objective, younger essential workers are prioritized to control spread or seniors to directly control mortality. When the objective is minimizing deaths, relative to an untargeted approach, prioritization averts deaths on a range between 20,000 (when nonpharmaceutical interventions are strong) and 300,000 (when these interventions are weak). We illustrate how optimal prioritization is sensitive to several factors, most notably, vaccine effectiveness and supply, rate of transmission, and the magnitude of initial infections.

The authority to manage natural capital often follows political boundaries rather than ecological. This mismatch can lead to unsustainable outcomes, as spillovers from one management area to the next may create adverse incentives for local decision making, even within a single country. At the same time, one-size-fits-all approaches of federal (centralized) authority can fail to respond to state (decentralized) heterogeneity and can result in inefficient economic or detrimental ecological outcomes. Here we utilize a spatially explicit coupled natural-human system model of a fishery to illuminate trade-offs posed by the choice between federal vs. state control of renewable resources. We solve for the dynamics of fishing effort and fish stocks that result from different approaches to federal management that vary in terms of flexibility. Adapting numerical methods from engineering, we also solve for the open-loop Nash equilibrium characterizing state management outcomes, where each state anticipates and responds to the choices of the others. We consider traditional federalism questions (state vs. federal management) as well as more contemporary questions about the economic and ecological impacts of shifting regulatory authority from one level to another. The key mechanisms behind the trade-offs include whether differences in local conditions are driven by biological or economic mechanisms; degree of flexibility embedded in the federal management; the spatial and temporal distribution of economic returns across states; and the status-quo management type. While simple rules-of-thumb are elusive, our analysis reveals the complex political economy dimensions of renewable resource federalism.

Scenario analysis has emerged as a key tool to analyze complex and uncertain future socio-ecological developments. However, currently existing global scenarios (narratives of how the world may develop) have neglected biological invasions, a major threat to biodiversity and the economy. Here, we use a novel participatory process to develop a diverse set of global biological invasion scenarios spanning a wide range of plausible global futures through to 2050. We adapted the widely used “two axes” scenario analysis approach to develop four families of four scenarios each, resulting in 16 scenarios that were later clustered into four contrasting sets of futures. Our analysis highlights that socioeconomic developments and technological innovation have the potential to shape biological invasions, in addition to well-known drivers, such as climate and human land use change and global trade. Our scenarios partially align with the shared socioeconomic pathways created by the climate change research community. Several factors that drive differences in biological invasions were underrepresented in the shared socioeconomic pathways; in particular, the implementation of biosecurity policies. We argue that including factors related to public environmental awareness and technological and trade development in global scenarios and models is essential to adequately consider biological invasions in global environmental assessments and thereby obtain a more integrative picture of future social–ecological developments.

International trade is a key pathway for the global spread of nonnative species. Historical and emerging trade flows interact with ecological dynamics to shape nonnative species risk and determine how that risk can be mitigated. This article discusses these underlying processes, emerging trade trends, and the role of past and future economics research in understanding and managing nonnative species risks from trade. We identify four priorities for future economics research. These include expanding economic analysis to consider interventions across the biosecurity continuum more comprehensively, leveraging new data systems for real-time prediction and effective allocation of inspection effort, applying economic analysis to anticipate and respond to emerging trade trends, and improving understanding of exporter and consumer behavioral responses to policy interventions in order to encourage intended (and ameliorate unintended) reactions.

Understanding the socioeconomic drivers of biological invasion informs policy development for curtailing future invasions. While early 20th-century plant trade expansions preceded increased establishments of plant pests in Northern America, increased establishments did not follow accelerating imports later that century. To explore this puzzle, we estimate the historical establishment of plant-feeding Hemiptera in Northern America as a function of historical U.S. imports of live plants from seven world regions. Delays between establishment and discovery are modeled using a previously unused proxy for dynamic discovery effort. By recovering the timing of pest arrivals from their historical discoveries, we disentangle the joint establishment-discovery process. We estimate long delays to discovery, which are partially attributable to the low detectability of less economically important insect species. We estimate that many introduced species remain undiscovered, ranging from around one-fifth for Eurasian regions to two-fifths for Central and South America.

Border inspections are a powerful tool for preventing pest invasions through the pathway of international trade. Inspection resources, however, are highly constrained, meaning only a small subset of incoming shipments is actually inspected. Inspection efficiency can be improved by targeting effort, for example, towards shipments of fresh produce and horticultural products from exporters with a history of sending contaminated products. While such targeting is well-understood and used by many importers, it is not effective for the targeting of novel and/or transient threats. In this study, we combine theory and empirical analysis to investigate how market-based signals such as price spikes can be exploited to fine-tune inspection effort. We build a simple theoretical model to analyse the behaviour within the supply-chain for perishable goods when heterogeneous manufacturers (e.g. hothouses) face capacity constraints and risk-mitigation is endogenous. The theoretical model suggests price fluctuations in the market may provide early warnings of increasing risk in a pathway. The underlying mechanism is that wholesalers may increase purchases from non-traditional sources when prices spike—sources that have no reputational risk from supplying infested products—thereby increasing the contamination rate for shipments received from a given shipper. The primary testable hypothesis is that price spikes correlate with higher infestation rates, especially when the price spikes are unanticipated. We test this hypothesis using data from the UK inspection agency on import inspections from 2010-2019. After controlling for seasonality, commodity and exporter fixed effects, we find a strong, positive link between price increases and pest infestation. Historically, profiling imports for pest risk has been limited to using the outcomes of prior inspection outcomes. Our analysis offers an important new tool for leveraging real-time market data to enhance the cost-effectiveness of scarce border inspections for the mitigation of damaging pests, especially from novel, emerging threats.

The COVID-19 pandemic brought unprecedented policy responses and a large literature evaluating their impacts. This paper re-examines and add to the evidence on the impact of COVID-19 mobility-restricting policies on mobility indicators. We first find that two-way fixed effects estimates are not robust to minor specification changes, where the same policy can be found to significantly increase or decrease mobility, depending on the specification. Therefore, due to the large number of researcher's degrees-of-flexibility, researchers can focus on a set of results that appears stable, while ignoring problematic ones. Further, recently developed heterogeneity-robust difference- in-differences methods only partially mitigate these issues.

In the absence of a vaccine, social distancing measures are one of the primary tools to reduce the transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, which causes coronavirus disease 2019 (COVID-19). We show that social distancing following US state-level emergency declarations substantially varies by income. Using mobility measures derived from mobile device location pings, we find that wealthier areas decreased mobility significantly more than poorer areas, and this general pattern holds across income quantiles, data sources, and mobility measures. Using an event study design focusing on behavior subsequent to state emergency orders, we document a reversal in the ordering of social distancing by income: Wealthy areas went from most mobile before the pandemic to least mobile, while, for multiple measures, the poorest areas went from least mobile to most. Previous research has shown that lower income communities have higher levels of preexisting health conditions and lower access to healthcare. Combining this with our core finding—that lower income communities exhibit less social distancing—suggests a double burden of the COVID-19 pandemic with stark distributional implications.

Understanding the likely future impacts of biological invasions is crucial yet highly challenging given the multiple relevant environmental, socio‐economic and societal contexts and drivers. In the absence of quantitative models, methods based on expert knowledge are the best option for assessing future invasion trajectories. Here, we present an expert assessment of the drivers of potential alien species impacts under contrasting scenarios and socioecological contexts through the mid‐21st century. Based on responses from 36 experts in biological invasions, moderate (20%–30%) increases in invasions, compared to the current conditions, are expected to cause major impacts on biodiversity in most socioecological contexts. Three main drivers of biological invasions—transport, climate change and socio‐economic change—were predicted to significantly affect future impacts of alien species on biodiversity even under a best‐case scenario. Other drivers (e.g. human demography and migration in tropical and subtropical regions) were also of high importance in specific global contexts (e.g. for individual taxonomic groups or biomes). We show that some best‐case scenarios can substantially reduce potential future impacts of biological invasions. However, rapid and comprehensive actions are necessary to use this potential and achieve the goals of the Post‐2020 Framework of the Convention on Biological Diversity.

AbstractEcosystems play an important role in supporting human welfare, including regulating the transmission of infectious diseases. Many of these services are not fully-appreciated due to complex environmental dynamics and lack of baseline data. Multicontinental amphibian decline due to the fungal pathogen Batrachochytrium dendrobatidis (Bd) provides a stark example. Even though amphibians are known to affect natural food webs—including mosquitoes that transmit human diseases—the human health impacts connected to their massive decline have received little attention. Here we show a causal link between a wave of Bd-driven collapse of amphibians in Central America and increased human malaria incidence. At the canton-level in Costa Rica and district-level in Panama, expected malaria incidence increased for eight years subsequent to amphibian losses, peaking at an additional 1.0 cases per 1,000 population (CPK). The increase is substantial in comparison to annual incidence levels from outbreaks in these countries, which peaked at 1.1-1.5 CPK during our period of study from 1976-2016. This pattern holds across multiple alternative approaches to the estimation model. This previously unidentified impact of biodiversity loss illustrates the often hidden human welfare costs of conservation failures. These findings also show the importance of mitigating international trade-driven spread of similar emergent pathogens like Batrachochytrium salamandrivorans.Significance StatementDespite substantial multicontinental collapses in amphibian populations from spread of the fungal pathogen Batrachochytrium dendrobatidis (Bd), the implications for humans have not been systematically studied. Amphibians are known to affect food webs, including mosquitoes that serve as a vector for the spread of disease. However, little is known about how their loss erodes ecosystem services, including the regulation of the transmission of infectious diseases. Using Central America as a case study, this study shows that Bd-driven amphibian loss led to a substantial increase in malaria incidence. The results highlight the often underappreciated social costs of biodiversity loss, including the potential stakes of ecosystem disruption from failing to stop spread of future novel pathogens.

AbstractMultiple promising COVID-19 vaccines are under rapid development, with deployment of the initial supply expected by 2021. Careful design of a vaccine prioritization strategy across socio-demographic groups is an imminent and crucial public policy challenge given that (1) the eventual vaccine supply will be highly constrained for at least the first several months of the vaccination campaign, and (2) there are stark differences in transmission and severity of impacts from SARS-CoV-2 across groups. Previous experience with vaccine development mid-pandemic offers limited insights for SARS-CoV-2 prioritization: SARS and Zika vaccine development was incomplete when those outbreaks ended and the epidemiology of endemic human influenza viruses differ substantially from that of SARS-CoV-2. We assess the optimal allocation of a limited and dynamic COVID-19 vaccine supply in the U.S. across socio-demographic groups differentiated by age and essential worker status. The transmission dynamics are modeled using a compartmental (SEIR) model parameterized to capture our current understanding of the transmission and epidemiological characteristics of COVID-19, including key sources of group heterogeneity (susceptibility, severity, and contact rates). We investigate tradeoffs between three alternative policy objectives: minimizing infections, years of life lost, or deaths. Moreover, we model dynamic vaccine prioritization policies that respond to changes in the epidemiological status of the population as SARS-CoV-2 continues its march. Because contacts tend to be concentrated within age groups, there is diminishing marginal returns as vaccination coverage increases in a given group, increasing the group’s protective immunity against infection and mortality. We find that optimal prioritization consistently targets older essential workers. However, depending on the policy objective, younger essential workers are prioritized to minimize infections or seniors in order to minimize mortality. Optimal prioritization outperforms non-targeted vaccination strategies by up to 18% depending on the outcome optimized. For example, in our baseline model, cumulative mortality decreased on average by 17% (25,000 deaths in the U.S. population) over the course of the outbreak.