Classifying fishing behavioral diversity using high-frequency movement data.

Effective management of social-ecological systems (SESs) requires an understanding of human behavior. In many SESs, there are hundreds of agents or more interacting with governance and regulatory institutions, driving management outcomes through collective behavior. Agents in these systems often display consistent behavioral characteristics over time that can help reduce the dimensionality of SES data by enabling the assignment of types. Typologies of resource-user behavior both enrich our knowledge of user cultures and provide critical information for management. Here, we develop a data-driven framework to identify resource-user typologies in SESs with high-dimensional data. To demonstrate policy applications, we apply the framework to a tightly coupled SES, commercial fishing. We leverage large fisheries-dependent datasets that include mandatory vessel logbooks, observer datasets, and high-resolution geospatial vessel tracking technologies. We first quantify vessel and behavioral characteristics using data that encode fishers' spatial decisions and behaviors. We then use clustering to classify these characteristics into discrete fishing behavioral types (FBTs), determining that 3 types emerge in our case study. Finally, we investigate how a series of disturbances applied selection pressure on these FBTs, causing the disproportionate loss of one group. Our framework not only provides an efficient and unbiased method for identifying FBTs in near real time, but it can also improve management outcomes by enabling ex ante investigation of the consequences of disturbances such as policy actions.

Disturbance modifies payoffs in the explore-exploit trade-off.

Decision-making agents face a fundamental trade-off between exploring new opportunities with risky outcomes versus exploiting familiar options with more certain but potentially suboptimal outcomes. Although mediation of this trade-off is essential to adaptive behavior and has for decades been assumed to modulate performance, the empirical consequences of human exploratory strategies are unknown beyond laboratory or theoretical settings. Leveraging 540,000 vessel position records from 2494 commercial fishing trips along with corresponding revenues, here we find that during undisturbed conditions, there was no relationship between exploration and performance, contrary to theoretical predictions. However, during a major disturbance event which closed the most-utilized fishing grounds, explorers benefited significantly from less-impacted revenues and were also more likely to continue fishing. We conclude that in stochastic natural systems characterized by non-stationary rewards, the role of exploration in buffering against disturbance may be greater than previously thought in humans.

Tradeoffs between fisheries harvest and the resilience of coral reefs.

Many countries are legally obliged to embrace ecosystem-based approaches to fisheries management. Reductions in bycatch and physical habitat damage are now commonplace, but mitigating more sophisticated impacts associated with the ecological functions of target fisheries species are in their infancy. Here we model the impacts of a parrotfish fishery on the future state and resilience of Caribbean coral reefs, enabling us to view the tradeoff between harvest and ecosystem health. We find that the implementation of a simple and enforceable size restriction of >30 cm provides a win:win outcome in the short term, delivering both ecological and fisheries benefits and leading to increased yield and greater coral recovery rate for a given harvest rate. However, maintaining resilient coral reefs even until 2030 requires the addition of harvest limitations (<10% of virgin fishable biomass) to cope with a changing climate and induced coral disturbances, even in reefs that are relatively healthy today. Managing parrotfish is not a panacea for protecting coral reefs but can play a role in sustaining the health of reefs and high-quality habitat for reef fisheries.

Disentangling trait-based mortality in species with decoupled size and age.

1. Size and age are fundamental organismal traits, and typically, both are good predictors of mortality. For many species, however, size and age predict mortality in ontogenetically opposing directions. Specifically, mortality due to predation is often more intense on smaller individuals whereas mortality due to senescence impacts, by definition, on older individuals. 2. When size-based and age-based mortality are independent in this manner, modelling mortality in both traits is often necessary. Classical approaches, such as Leslie or Lefkovitch matrices, usually require the model to infer the state of one trait from the state of the other, for example by assuming that explicitly modelled age (or stage) class structure provides implicit information on underlying size-class structure, as is the case in many species. 3. However, the assumption that one trait informs on the other is challenged when size and age are decoupled, as often occurs in invertebrates, amphibians, fish, reptiles and plants. In these cases, age-structured models may perform poorly at capturing size-based mortality, and vice versa. 4. We offer a solution to this dilemma, relaxing the assumption that class structure in one trait is inferable from class structure in another trait. Using empirical data from a reef fish, Sparisoma viride (Scaridae), we demonstrate how an individual-based model (IBM) can be implemented to model mortality as explicit, independent and simultaneous functions of individual size and age - an approach that mimics the effects of mortality in many wild populations. By validating this 'multitrait IBM' against three independent lines of empirical data, we determine that the approach produces more convincing predictions of size-class structure, longevity and post-settlement mortality for S. viride than do the trait-independent or single-trait mortality models tested. 5. Multitrait IBMs also allow trait-based mortality to be modelled either additively or multiplicatively, and individual variability in growth rates can be accommodated. Consequently, we propose that the approach may be useful in fields that may benefit from disentangling, or investigating interactions among, size-based and age-based demographic processes, including comparative demography (e.g. life-history consequences of resource patchiness) and conservation biology (e.g. impacts of invasive predators on size structure but not life span of natives).