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Non-lethal injury in animals is both common and costly. The cost of regenerating autotomized limbs may leave less energy available for processes such as reproduction and growth, leading to trade-offs. Such trade-offs are context-dependent, and an individual's energy allocation strategies may vary widely based on its condition and the environment. However, many traditional bioenergetics models have relied on fixed energy allocation rules, such as the -rule of dynamic energy budget theory, which assumes a fixed proportion (κ) of assimilated energy is always allocated to growth and maintenance. To determine whether incorporating optimality approaches into bioenergetics models improves the ability to predict energy allocation, we developed a dynamic state variable model that identifies optimal limb regeneration strategies in a model system, the Asian shore crab Hemigrapsus sanguineus. Our model predictions align with known patterns for this species, including increased regeneration effort with injury severity, a shift from reproduction to growth as consumption amount increases, and an increase in regeneration effort as regeneration progresses. Lastly, Monte Carlo simulations of individuals from a previous experiment demonstrate that flexible energy allocation successfully predicts reproductive effort, suggesting that this approach may improve the accuracy of bioenergetics modeling. [ABSTRACT FROM AUTHOR]

1. During their seasonal migration, birds stage in areas comprising stopover sites of varying quality. Given that migrating birds have a limited information about their environment, they may land at a low-quality stopover site in which their fuel deposition rate (FDR) is low. Birds landing at such sites should decide either to extend their stopover duration or to quickly depart in search for a better site. These decisions, however, strongly depend on their body condition upon landing. 2. To understand the decision-making process of passerines within a stopover area, comprising stopover sites of varying quality, prior to the crossing of a large ecological barrier, we constructed a state-dependent habitat selection model. The model assumes that even if migrating birds have an expectation of encountered area quality, they cannot control for their initial landing site. Once landing, movement between low-and high-quality stopover sites will occur only if the body condition of these birds is high to the extent that they can entail the energetic cost of movement. Birds in the model aim to maximize their fuel load at the end of the stopover period, to suffice for successfully crossing a large ecological barrier. 3. The model is based on empirical data on autumn migrating Blackcaps Sylvia atricapilla, collected at two important stopover sites in the Negev desert of Israel. Migrating passerines staging at these two sites differ in their FDR and body condition. The model shows that the optimal behaviour when arriving at a low-quality stopover site is to abandon it quickly. However, as lean individuals cannot entail the costs of searching for an alternative site, they have no other choice but to stay there even if their chances to successfully cross the Sahara Desert ahead are low. 4. Our model can be applied to other ecological systems. Proper use of this model may allow good assessment of stopover site quality, as indicated by the bird's FDR, regardless of specific site characteristics. Hence, it can help applying targeted management decisions regarding the maintenance of stopover sites or establishment of new ones. [ABSTRACT FROM AUTHOR]

As declines in biodiversity accelerate, there is an urgent imperative to ensure that every dollar spent on conservation counts toward species protection. Systematic conservation planning is a widely used approach to achieve this, but there is growing concern that it must better integrate the human social dimensions of conservation to be effective. Yet, fundamental insights about when social data are most critical to inform conservation planning decisions are lacking. To address this problem, we derived novel principles to guide strategic investment in social network information for systematic conservation planning. We considered the common conservation problem of identifying which social actors, in a social network, to engage with to incentivize conservation behavior that maximizes the number of species protected. We used simulations of social networks and species distributed across network nodes to identify the optimal state‐dependent strategies and the value of social network information. We did this for a range of motif network structures and species distributions and applied the approach to a small‐scale fishery in Kenya. The value of social network information depended strongly on both the distribution of species and social network structure. When species distributions were highly nested (i.e., when species‐poor sites are subsets of species‐rich sites), the value of social network information was almost always low. This suggests that information on how species are distributed across a network is critical for determining whether to invest in collecting social network data. In contrast, the value of social network information was greatest when social networks were highly centralized. Results for the small‐scale fishery were consistent with the simulations. Our results suggest that strategic collection of social network data should be prioritized when species distributions are un‐nested and when social networks are likely to be centralized. Article impact statement: The value of collecting social network information for conservation planning depends on both species distributions and social network structure. [ABSTRACT FROM AUTHOR]

Foraging in uncertain environments requires balancing the risks associated with finding alternative resources against potential gains. In arid-land environments characterized by extreme variation in the amount and seasonal timing of primary production, consumers must weigh the risks associated with foraging for preferred seeds that can be cached against fallback foods of low nutritional quality (e.g., leaves) that must be consumed immediately. Here, we explore the influence of resource scarcity, body size, and seasonal uncertainty on the expected foraging behaviors of caching rodents in the northern Chihuahaun Desert by integrating these elements with a stochastic dynamic program to determine fitness-maximizing foraging strategies. We demonstrate that resource-limited environments promote dependence on fallback foods, reducing the likelihood of starvation while increasing future risk exposure. Our results point to a qualitative difference in the use of fallback foods and the fitness benefits of caching at the threshold body size of 50 g. Above this 50-g body size threshold, we observe large fitness gains associated with the maintenance of even a modest-sized cache, whereas similar gains for smaller consumers require maintenance of unrealistically large caches. This suggests that larger-bodied consumers that cache may be less sensitive to the future uncertainties in monsoonal onset predicted by global climate scenarios, whereas smaller consumers, regardless of caching behavior, may be at greater risk. [ABSTRACT FROM AUTHOR]

We develop a method for finding optimal greenhouse gas reduction rates under ongoing uncertainty and re-evaluation of climate parameters over future decades. Uncertainty about climate change includes both overall climate sensitivity and the risk of extreme tipping point events. We incorporate both types of uncertainty into a stochastic model of climate and the economy that has the objective of reducing global greenhouse gas emissions at lowest overall cost over time. Solving this problem is computationally challenging; we introduce a two-step-ahead approximate dynamic programming algorithm to solve the finite time horizon stochastic problem. The uncertainty in climate sensitivity may narrow in the future as the behavior of the climate continues to be observed and as climate science progresses. To incorporate this future knowledge, we use a Bayesian framework to update the two correlated uncertainties over time. The method is illustrated with the DICE integrated assessment model, adding in current estimates of climate sensitivity uncertainty and tipping point risk with an endogenous updating of climate sensitivity based on the occurrence of tipping point events; the method could also be applied to other integrated assessment models with different characterizations of uncertainties and risks. [ABSTRACT FROM AUTHOR]

Captive breeding of animals is widely used to manage endangered species, frequently with the ambition of future reintroduction into the wild. Because this conservation measure is very expensive, we need to optimize decisions, such as when to capture wild animals or release captive-bred individuals into the wild. It is unlikely that one particular strategy will always work best; instead, we expect the best decision to depend on the number of individuals in the wild and in captivity. We constructed a first-order Markov-chain population model for two populations, one captive and one wild, and we used stochastic dynamic programming to identify optimal state-dependent strategies. The model recommends unique sequences of optimal management actions over several years. A robust rule of thumb for species that can increase faster in captivity than in the wild is to capture the entire wild population whenever the wild population is below a threshold size of 20 females. This rule applies even if the wild population is growing and under a broad range of different parameter values. Once a captive population is established, it should be maintained as a safety net and animals should be released only if the captive population is close to its carrying capacity. We illustrate the utility of this model by applying it to the Arabian oryx (Oryx leucoryx). The threshold for capturing the entire Arabian oryx population in the wild is 36 females, and captive-bred individuals should not be released before the captive facilities are at least 85% full. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study the monotonicity properties with respect to the probability distribution of the state processes, of optimal decisions in bandit decision problems. Orderings of dynamic discrete projects are provided by extending the notion of stochastic dominance to stochastic processes. [ABSTRACT FROM AUTHOR]