If larvae were smart: a simple model for optimal settlement behavior of competent larvae

Much research has been done on larval settlement cues. Rather than having simple fixed responses to constant environmental stimuli, it seems likely that settlement decisions made by individual larvae should vary depending on the individual and the conditions under which it encounters that cue. Here, we present a simple stochastic dynamic programming model that explores the conditions under which larvae may maximize their lifetime fitness by accepting lower quality habitat rather than continuing to search for superior habitat. Our model predicts that there is a relatively narrow range of parameter values over which larval selectivity among habitat types changes dramatically from 1 (larvae accept only optimal substrata) to 0 (indiscriminant settlement). This narrow range coincides with our best estimate of parameter values gleaned from empirical studies, and the model output matches data for the polychaete worm Hydroides dianthus remarkably well. The relative availability of habitats and the total time available to search for high quality habitat (i.e. the ability to delay metamorphosis) had the greatest effects on larval selectivity. In contrast, intuitive factors, including larval energetics and mortality, showed little effect on larval habitat preference, but could still alter the proportion of larvae settling in different habitats by reducing search time. Our model predicts that a given larva may behave differently depending on where it falls in the optimality decision matrix at the instant in which it locates substrata. This model provides a conceptual framework in which to conduct future studies involving variability in settlement decisions among individual larvae, and in which to consider the selective forces driving the evolution of specific larval settlement cues. Our results suggest that a combination of the maximum search period and the relative frequency and quality of optimal habitat likely exert the greatest influence on the evolution of larval selectivity in the field

Maximizing a new quantity in sequential reserve selection

The fundamental goal of conservation planning is biodiversity persistence, yet most reserve selection methods prioritize sites using occurrence data. Numerous empirical studies support the notion that defining and measuring objectives in terms of species richness (where the value of a site is equal to the number of species it contains, or contributes to an existing reserve network) can be inadequate for maintaining biodiversity in the long term. An existing site-assessment framework that implicitly maximized the persistence probability of multiple species was integrated with a dynamic optimization model. The problem of sequential reserve selection as a Markov decision process was combined with stochastic dynamic programming to find the optimal solution. The approach represents a compromise between representation-based approaches (maximizing occurrences) and more complex tools, like spatially-explicit population models. The method, the inherent problems and interesting conclusions are illustrated with a land acquisition case study on the central Platte River

Stochastic Variation in Food Availability Influences Weight and Age at Maturity

Variation in mean food availability, and in the variance around the mean, affects the growth rate during development. Previous theoretical work on the influence of environmental quality or growth rates on the phenotypic traits age and size at maturation assumed that there is no variation in growth rate or food availability within a generation. We develop a stochastic dynamic programming (SDP) model of the foraging behavior of aphidophagous syrphids, and use this model to predict when syrphids should pupate (mature) when average food availability changes, or varies stochastically, during development. The optimal strategy takes into account not only the availability of food, but also the timing of its availability. Food availability, when small, influences developmental time, but not weight at pupation. Food availability, when large, influences weight at pupation, but not developmental time. When the food supply is low, the optimal strategy adjusts the size at pupation downwards for stochastic as opposed to deterministic availability of food. The conclusions reinforce the need for lifehistory studies to consider state dependence and short-term variability in growth rates

Stochastic Variation in Food Availability Influences Weight and Age at Maturity

Variation in mean food availability, and in the variance around the mean, affects the growth rate during development. Previous theoretical work on the influence of environmental quality or growth rates on the phenotypic traits age and size at maturation assumed that there is no variation in growth rate or food availability within a generation. We develop a stochastic dynamic programming (SDP) model of the foraging behavior of aphidophagous syrphids, and use this model to predict when syrphids should pupate (mature) when average food availability changes, or varies stochastically, during development. The optimal strategy takes into account not only the availability of food, but also the timing of its availability. Food availability, when small, influences developmental time, but not weight at pupation. Food availability, when large, influences weight at pupation, but not developmental time. When the food supply is low, the optimal strategy adjusts the size at pupation downwards for stochastic as opposed to deterministic availability of food. The conclusions reinforce the need for lifehistory studies to consider state dependence and short-term variability in growth rates

Abundance estimation from multiple data types for groupliving animals: An example using dhole (Cuon alpinus)

Large carnivores are declining globally and require baseline population estimates for management, however large-scale population estimation is problematic for species without unique natural marks. We used camera trap records of dhole Cuon alpinus, a group-living species, from three national parks in Thailand as a case study in which we develop integrated likelihood models to estimate abundance incorporating two different data sets, count data and detection/non-detection data. We further investigated relative biases of the models using different proportions of data with lower versus higher quality and assessed parameter identifiability. The simulations indicated that the relative bias on average across 24 tested scenarios was 2% with a 95% chance that the simulated data sets obtained the true animal abundances. We found that bias was high (\u3e10%) when sampling 60 sites with only 5 sampling occasions. We tested four additional scenarios with varying proportions of count data. Our model tolerated the use of relatively low proportions of the higher quality count data, but below 10% the results began to show bias (\u3e6%). Data cloning indicated that the parameters were identifiable with all posterior variances shrinking to near zero. Our model demonstrates the benefits of combining data from multiple studies even with different data types. Furthermore, the approach is not limited to camera trap data. Detection/non-detection data from track surveys or counts from transects could also be combined. Particularly, our model is potentially useful for assessing populations of rare species where large amounts of by-catch datasets are available

Inferring process from pattern: Can territory occupancy provide information about life history parameters?

A significant problem in wildlife management is identifying "good" habitat for species within the short time frames demanded by policy makers. Statistical models of the response of species presence/absence to predictor variables are one solution, widely known as habitat modeling. We use a "virtual ecologist" to test logistic regression as a means of developing habitat models within a spatially explicit, individual-based simulation that allows habitat quality to influence either fecundity or survival with a continuous scale. The basic question is how good are logistic regression models of habitat quality at identifying habitat where birth rates are high and death rates low (i.e., "source" habitat)? We find that, even when all the important variables are perfectly measured, and there is no error in surveying the species of interest, demographic stochasticity and the limiting effect of localized dispersal generally prevent an explanation of much more than half of the variation in territory occupancy as a function of habitat quality. This is true regardless of whether fecundity or survival is influenced by habitat quality. In addition, habitat models only detect a significant effect of habitat on territory occupancy when habitat quality is spatially autocorrelated. We find that habitat models based on logistic regression really measure the ability of the species to reach and colonize areas, not birth or death rates

CHANNEL WIDTH AND LEAST TERN AND PIPING PLOVER NESTING INCIDENCE ON THE LOWER PLATTE RIVER, NEBRASKA

Endangered interior least terns (Sternula antillarum athalassos) and threatened northern Great Plains piping plovers (Charadrius melodus) nest together on midstream sandbars in large rivers in the interior of North America. We investigated the relationship between river channel width and tern and plover nesting incidence on the lower Platte River, Nebraska, using a model-based logistic regression analysis. Multiple channel width measurements and a long-term nesting data set were used in the analysis. Nesting incidence was positively associated with increasing river channel width proximal to the nesting site. At a greater distance, up to 802 m away from the nesting site, there was no relationship with channel width. Managers and regulators should use these results to aid decisions pertaining to habitat creation and assessing impacts of future projects. Future research should address whether relationships exist between river channel width and nest counts and reproductive rates of interior least tern and piping plovers on the lower Platte River

CHANNEL WIDTH AND LEAST TERN AND PIPING PLOVER NESTING INCIDENCE ON THE LOWER PLATTE RIVER, NEBRASKA

Endangered interior least terns (Sternula antillarum athalassos) and threatened northern Great Plains piping plovers (Charadrius melodus) nest together on midstream sandbars in large rivers in the interior of North America. We investigated the relationship between river channel width and tern and plover nesting incidence on the lower Platte River, Nebraska, using a model-based logistic regression analysis. Multiple channel width measurements and a long-term nesting data set were used in the analysis. Nesting incidence was positively associated with increasing river channel width proximal to the nesting site. At a greater distance, up to 802 m away from the nesting site, there was no relationship with channel width. Managers and regulators should use these results to aid decisions pertaining to habitat creation and assessing impacts of future projects. Future research should address whether relationships exist between river channel width and nest counts and reproductive rates of interior least tern and piping plovers on the lower Platte River

Modelling Dispersal Behaviour on a Fractal Landscape

We use a spatially explicit population model to explore the population consequences of different habitat selection mechanisms on landscapes with fractal variation in habitat quality. We consider dispersal strategies ranging from random walks to perfect habitat selectors for two species of arboreal marsupial, the greater glider (Petauroides volans) and the mountain brushtail possum (Trichosurus caninus). In this model increasing habitat selection means individuals obtain higher quality territories, but experience increased mortality during dispersal. The net effect is that population sizes are smaller when individuals actively select habitat. We find positive relationships between habitat quality and population size can occur when individuals do not use information about the entire landscape when habitat quality is spatially autocorrelated. We also find that individual behaviour can mitigate the negative effects of spatial variation on population average survival and fecundity