Cost-benefit analysis of ecosystem modeling to support fisheries management.

Mathematical and statistical models underlie many of the world's most important fisheries management decisions. Since the 19th century, difficulty calibrating and fitting such models has been used to justify the selection of simple, stationary, single-species models to aid tactical fisheries management decisions. Whereas these justifications are reasonable, it is imperative that we quantify the value of different levels of model complexity for supporting fisheries management, especially given a changing climate, where old methodologies may no longer perform as well as in the past. Here we argue that cost-benefit analysis is an ideal lens to assess the value of model complexity in fisheries management. While some studies have reported the benefits of model complexity in fisheries, modeling costs are rarely considered. In the absence of cost data in the literature, we report, as a starting point, relative costs of single-species stock assessment and marine ecosystem models from two Australian organizations. We found that costs varied by two orders of magnitude, and that ecosystem model costs increased with model complexity. Using these costs, we walk through a hypothetical example of cost-benefit analysis. The demonstration is intended to catalyze the reporting of modeling costs and benefits.

Unrecognized threat to global soil carbon by a widespread invasive species.

Most of Earth's terrestrial carbon is stored in the soil and can be released as carbon dioxide (CO2 ) when disturbed. Although humans are known to exacerbate soil CO2 emissions through land-use change, we know little about the global carbon footprint of invasive species. We predict the soil area disturbed and resulting CO2 emissions from wild pigs (Sus scrofa), a pervasive human-spread vertebrate that uproots soil. We do this using models of wild pig population density, soil damage, and their effect on soil carbon emissions. Our models suggest that wild pigs are uprooting a median area of 36,214 km2 (mean of 123,517 km2 ) in their non-native range, with a 95% prediction interval (PI) of 14,208 km2 -634,238 km2 . This soil disturbance results in median emissions of 4.9 million metric tonnes (MMT) CO2 per year (equivalent to 1.1 million passenger vehicles or 0.4% of annual emissions from land use, land-use change, and forestry; mean of 16.7 MMT) but that it is highly uncertain (95% PI, 0.3-94 MMT CO2 ) due to variability in wild pig density and soil dynamics. This uncertainty points to an urgent need for more research on the contribution of wild pigs to soil damage, not only for the reduction of anthropogenically related carbon emissions, but also for co-benefits to biodiversity and food security that are crucial for sustainable development.

Invasive wild pigs (Sus scrofa) as a human-mediated source of soil carbon emissions: Uncertainties and future directions.

Invasive wild pigs (Sus scrofa) have been spread by humans outside of their native range and are now established on every continent except Antarctica. Through their uprooting of soil, they affect societal and environmental values. Our recent article explored another threat from their soil disturbance: greenhouse gas emissions (O'Bryan et al., Global Change Biology, 2021). In response to our paper, Don (Global Change Biology, 2021) claims there is no threat to global soil carbon stocks by wild pigs. While we did not investigate soil carbon stocks, we examine uncertainties regarding soil carbon emissions from wild pig uprooting and their implications for management and future research.

Poacher-population dynamics when legal trade of naturally deceased organisms funds anti-poaching enforcement.

Can a regulated, legal market for wildlife products protect species threatened by poaching? It is one of the most controversial ideas in biodiversity conservation. Perhaps the most convincing reason for legalizing wildlife trade is that trade revenue could fund the protection and conservation of poached species. In this paper, we examine the possible poacher-population dynamic consequences of legal trade funding conservation. The model consists of a manager scavenging carcasses for wildlife product, who then sells the product, and directs a portion of the revenue towards funding anti-poaching law enforcement. Through a global analysis of the model, we derive the critical proportion of product the manager must scavenge, and the critical proportion of trade revenue the manager must allocate towards increased enforcement, in order for legal trade to lead to abundant long-term wildlife populations. We illustrate how the model could inform management with parameter values derived from the African elephant literature, under a hypothetical scenario where a manager scavenges elephant carcasses to sell ivory. We find that there is a large region of parameter space where populations go extinct under legal trade unless a significant portion of trade revenue is directed towards protecting populations from poaching. The model is general and therefore can be used as a starting point for exploring the consequences of funding many conservation programs using wildlife trade revenue.

Informing management decisions for ecological networks, using dynamic models calibrated to noisy time-series data.

Well-intentioned environmental management can backfire, causing unforeseen damage. To avoid this, managers and ecologists seek accurate predictions of the ecosystem-wide impacts of interventions, given small and imprecise datasets, which is an incredibly difficult task. We generated and analysed thousands of ecosystem population time series to investigate whether fitted models can aid decision-makers to select interventions. Using these time-series data (sparse and noisy datasets drawn from deterministic Lotka-Volterra systems with two to nine species, of known network structure), dynamic model forecasts of whether a species' future population will be positively or negatively affected by rapid eradication of another species were correct > 70% of the time. Although 70% correct classifications is only slightly better than an uninformative prediction (50%), this classification accuracy can be feasibly improved by increasing monitoring accuracy and frequency. Our findings suggest that models may not need to produce well-constrained predictions before they can inform decisions that improve environmental outcomes.

The mesoscavenger release hypothesis and implications for ecosystem and human well-being.

Many apex scavenger species, including nearly all obligate scavengers, are in a state of rapid decline and there is growing evidence these declines can drastically alter ecological food webs. Our understanding of how apex scavengers regulate populations of mesoscavengers, those less-efficient scavengers occupying mid-trophic levels, is improving; yet, there has been no comprehensive evaluation of the evidence around the competitive release of these species by the loss of apex scavengers. Here we present current evidence that supports the mesoscavenger release hypothesis, the increase in mesoscavengers and increase in carrion in the face of declining apex scavengers. We provide two models of scavenger dynamics to demonstrate that the mesoscavenger release hypothesis is consistent with ecological theory. We further examine the ecological and human well-being implications of apex scavenger decline, including carrion removal and disease regulation services.

Forecasting species range dynamics with process-explicit models: matching methods to applications.

Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process-explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process-explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs - regulatory planning, extinction risk, climate refugia and invasive species - we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process-explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.

Reach and messages of the world's largest ivory burn.

Recent increases in ivory poaching have depressed African elephant populations. Successful enforcement has led to ivory stockpiling. Stockpile destruction is becoming increasingly popular, and most destruction has occurred in the last 5 years. Ivory destruction is intended to send a strong message against ivory consumption, both in promoting a taboo on ivory use and catalyzing policy change. However, there has been no effort to establish the distribution and extent of media reporting on ivory destruction events globally. We analyzed media coverage of the largest ivory destruction event in history (Kenya, 30 April 2016) across 11 nation states connected to ivory trade. We used an online-media crawling tool to search online media outlets and subjected 5 of the largest print newspapers (by circulation) in 5 nations of interest to content analysis. Most online news on the ivory burn came from the United States (81% of 1944 articles), whereas most of the print news articles came from Kenya (61% of 157 articles). Eighty-six to 97% of all online articles reported the burn as a positive conservation action, whereas 4-50% discussed ivory burning as having a negative impact on elephant conservation. Most articles discussed law enforcement and trade bans as effective for elephant conservation. There was more relative search interest globally in the 2016 Kenyan ivory burn than any other burn in 5 years. Ours is the first attempt to track the reach of media coverage relative to an ivory burn and provides a case study in tracking the effects of a conservation-marketing event.

High prices for rare species can drive large populations extinct: the anthropogenic Allee effect revisited.

Consumer demand for plant and animal products threatens many populations with extinction. The anthropogenic Allee effect (AAE) proposes that such extinctions can be caused by prices for wildlife products increasing with species rarity. This price-rarity relationship creates financial incentives to extract the last remaining individuals of a population, despite higher search and harvest costs. The AAE has become a standard approach for conceptualizing the threat of economic markets on endangered species. Despite its potential importance for conservation, AAE theory is based on a simple graphical model with limited analysis of possible population trajectories. By specifying a general class of functions for price-rarity relationships, we show that the classic theory can understate the risk of species extinction. AAE theory proposes that only populations below a critical Allee threshold will go extinct due to increasing price-rarity relationships. Our analysis shows that this threshold can be much higher than the original theory suggests, depending on initial harvest effort. More alarmingly, even species with population sizes above this Allee threshold, for which AAE predicts persistence, can be destined to extinction. Introducing even a minimum price for harvested individuals, close to zero, can cause large populations to cross the classic anthropogenic Allee threshold on a trajectory towards extinction. These results suggest that traditional AAE theory may give a false sense of security when managing large harvested populations.

Human judgment vs. quantitative models for the management of ecological resources.

Despite major advances in quantitative approaches to natural resource management, there has been resistance to using these tools in the actual practice of managing ecological populations. Given a managed system and a set of assumptions, translated into a model, optimization methods can be used to solve for the most cost-effective management actions. However, when the underlying assumptions are not met, such methods can potentially lead to decisions that harm the environment and economy. Managers who develop decisions based on past experience and judgment, without the aid of mathematical models, can potentially learn about the system and develop flexible management strategies. However, these strategies are often based on subjective criteria and equally invalid and often unstated assumptions. Given the drawbacks of both methods, it is unclear whether simple quantitative models improve environmental decision making over expert opinion. In this study, we explore how well students, using their experience and judgment, manage simulated fishery populations in an online computer game and compare their management outcomes to the performance of model-based decisions. We consider harvest decisions generated using four different quantitative models: (1) the model used to produce the simulated population dynamics observed in the game, with the values of all parameters known (as a control), (2) the same model, but with unknown parameter values that must be estimated during the game from observed data, (3) models that are structurally different from those used to simulate the population dynamics, and (4) a model that ignores age structure. Humans on average performed much worse than the models in cases 1-3, but in a small minority of scenarios, models produced worse outcomes than those resulting from students making decisions based on experience and judgment. When the models ignored age structure, they generated poorly performing management decisions, but still outperformed students using experience and judgment 66% of the time.

Overcoming the impossibility of age-balanced harvest.

In many countries, sustainability targets for managed fisheries are often expressed in terms of a fixed percentage of the carrying capacity. Despite the appeal of such a simple quantitative target, an unintended consequence may be a significant tilting of the proportions of biomass across different ages, from what they would have been under harvest-free conditions. Within the framework of a widely used age-structured model, we propose a novel quantitative definition of "age-balanced harvest" that considers the age-class composition relative to that of the unfished population. We show that achieving a perfectly age-balanced policy is impossible if we harvest any fish whatsoever. However, every non-trivial harvest policy has a special structure that favours the young. To quantify the degree of age-imbalance, we propose a cross-entropy function. We formulate an optimisation problem that aims to attain an "age-balanced steady state", subject to adequate yield. We demonstrate that near balanced harvest policies are achievable by sacrificing a small amount of yield. These findings have important implications for sustainable fisheries management by providing insights into trade-offs and harvest policy recommendations.

Optimal control and cold war dynamics between plant and herbivore.

Herbivores eat the leaves that a plant needs for photosynthesis. However, the degree of antagonism between plant and herbivore may depend critically on the timing of their interactions and the intrinsic value of a leaf. We present a model that investigates whether and when the timing of plant defense and herbivore feeding activity can be optimized by evolution so that their interactions can move from antagonistic to neutral. We assume that temporal changes in environmental conditions will affect intrinsic leaf value, measured as potential carbon gain. Using optimal-control theory, we model herbivore evolution, first in response to fixed plant strategies and then under coevolutionary dynamics in which the plant also evolves in response to the herbivore. In the latter case, we solve for the evolutionarily stable strategies of plant defense induction and herbivore hatching rate under different ecological conditions. Our results suggest that the optimal strategies for both plant and herbivore are to avoid direct conflict. As long as the plant has the capability for moderately lethal defense, the herbivore will modify its hatching rate to avoid plant defenses, and the plant will never have to use them. Insights from this model offer a possible solution to the paradox of sublethal defenses and provide a mechanism for stable plant-herbivore interactions without the need for natural enemy control.

Is Mushy Tuna Syndrome a Growing Problem for the Tuna Industry?

Reducing food loss and waste is crucial for a sustainable global food system and an efficient use of natural resources. Fast-growing tuna provides a key contribution to global nutrition targets; however, reports suggest that an appreciable proportion of the catch is lost from its value chain due to flesh quality issues, one of which is Mushy Tuna Syndrome (MTS). MTS-affected tuna flesh becomes soft and pasty, unfit for canning or human consumption, resulting in high wastage of partially processed material. We investigated the prevalence of MTS globally by surveying the tuna industry using a questionnaire. Of the responses from 32 companies across 14 nations, 97% acknowledged MTS as an issue that predominantly affects skipjack (Katsuwonus pelamis) tuna. The cost of rejects reported by participants from 2017 to 2019 varied greatly, from less than 1000 USD per year to over 1 million USD. The median cost was over 60,000 USD and the average rejection rate was 1.8%. The occurrence of MTS was noted to be seasonal, mainly in the summer months. More than half of the respondents who experience MTS reported an increasing trend of occurrence. Industry perceptions suggest MTS causes are associated with environmental, physiological, and biological factors. The survey results highlight that MTS is prevalent in the industry and demonstrate the need to identify amelioration strategies for the fishers and processors to minimise loss and maximise resource efficiency.

A decision tree for assessing the risks and benefits of publishing biodiversity data.

Inadequate information on the geographical distribution of biodiversity hampers decision-making for conservation. Major efforts are underway to fill knowledge gaps, but there are increasing concerns that publishing the locations of species is dangerous, particularly for species at risk of exploitation. While we recognize that well-informed control of location data for highly sensitive taxa is necessary to avoid risks, such as poaching or habitat disturbance by recreational visitors, we argue that ignoring the benefits of sharing biodiversity data could unnecessarily obstruct conservation efforts for species and locations with low risks of exploitation. We provide a decision tree protocol for scientists that systematically considers both the risks of exploitation and potential benefits of increased conservation activities. Our protocol helps scientists assess the impacts of publishing biodiversity data and aims to enhance conservation opportunities, promote community engagement and reduce duplication of survey efforts.

Optimal escapement in stage-structured fisheries with environmental stochasticity.

Stage-structured population models are commonly used to understand fish population dynamics and additionally for stock assessment. Unfortunately, there is little theory on the optimal harvest of stage-structured populations, especially in the presence of stochastic fluctuations. In this paper, we find closed form optimal equilibrium escapement policies for a three-dimensional, discrete-time, stage-structured population model with linear growth, post-harvest nonlinear recruitment, and stage-specific pricing and extend the analytic results to structured populations with environmental stochasticity. When only fishing reproductive adults, stochasticity does not affect optimal escapement policies. However, when harvesting immature fish, the addition of stochasticity can increase or decrease optimal escapement depending on the second and third derivative of the recruitment function. For logistic recruitment, stochasticity reduces optimal immature escapement by a multiplicative factor of one over one plus the variance of the environmental noise. Using hard clam, Mercenaria mercenaria, as an example and assuming Beverton-Holt recruitment, we show that optimal fishing of hard clam targets the immature stage class exclusively and that environmental stochasticity increases optimal escapement for low discount rates and decreases optimal escapement for high discount rates.