Imperfectly optimal animals

Summary We consider models of behavior that apply to two different problems: when a predator should leave a foraging site and how a female should choose the best available male. In each case we derive rules for an optimal solution to the problem. We also derive models based on very simple, plausible rules of behavior that we suspect animals may actually use. Although the expected payoffs from optimality models always exceed the expected payoffs from our simpler behavioral models, under certain conditions the difference is not large. When good foraging sites last but a short time and when females' mobility in their habitat is limited, the results of simple models and optimal models are very close indeed.Because of the difficulty of distinguishing between the results of each type of model and because natural selection will presumably provide a best mix of solutions to a range of problems rather than a best solution to any one problem, we suggest that behavioral ecologists expend more effort on simple, plausible models of animal behavior. Such models provide ready-made testable hypotheses about the animal's approximation to optimality and about the actual mechanisms of behavior.     

Human preferences for facial masculinity change with relationship type and environmental harshness

In humans (Homo sapiens), sexual dimorphism in face shape has been proposed to be linked to quality in both men and women. Although preferences for high-quality mates might be expected, previous work has suggested that high quality may be associated with decreased investment in partnerships. In line with a trade-off between partner quality and investment, human females have been found to prefer higher levels of masculinity when judging under conditions where the benefits of quality would be maximised and the costs of low investment would be minimised. In this study, we examined facultative preferences for masculinity/femininity under hypothetical high and low environmental harshness in terms of resource availability in which participants were asked to imagine themselves in harsh/safe environments. We demonstrate that environmental harshness influences preferences for sexual dimorphism differently according to whether the relationship is likely to be short or long term. Women prefer less-masculine male faces and men prefer less-feminine female faces for long-term than short-term relationships under conditions of environmental harshness. Such findings are consistent with the idea that high-quality partners may be low investors and suggest that under harsh ecological conditions, both men and women favour a low-quality/high-investment partner for long-term relationships. For short-term relationships, where investment is not an important variable, preferences for sexual dimorphism were similar for the low and high environmental harshness conditions. These results provide experimental evidence that human preferences may be contingent on the environment an individual finds itself inhabiting.     

Estimating the abundance of mouse populations of known size: promises and pitfalls of new methods.

Knowledge of animal abundance is fundamental to many ecological studies. Frequently, researchers cannot determine true abundance, and so must estimate it using a method such as mark-recapture or distance sampling. Recent advances in abundance estimation allow one to model heterogeneity with individual covariates or mixture distributions and to derive multimodel abundance estimators that explicitly address uncertainty about which model parameterization best represents truth. Further, it is possible to borrow information on detection probability across several populations when data are sparse. While promising, these methods have not been evaluated using mark-recapture data from populations of known abundance, and thus far have largely been overlooked by ecologists. In this paper, we explored the utility of newly developed mark-recapture methods for estimating the abundance of 12 captive populations of wild house mice (Mus musculus). We found that mark-recapture methods employing individual covariates yielded satisfactory abundance estimates for most populations. In contrast, model sets with heterogeneity formulations consisting solely of mixture distributions did not perform well for several of the populations. We show through simulation that a higher number of trapping occasions would have been necessary to achieve good estimator performance in this case. Finally, we show that simultaneous analysis of data from low abundance populations can yield viable abundance estimates.     

Urea Fluctuations in Stream Baseflow across Land Cover Gradients and Seasons in a Coastal Plain River System

Urea‐N is a component of bioavailable dissolved organic nitrogen (DON) that contributes to coastal eutrophication. In this study, we assessed urea‐N in baseflow across land cover gradients and seasons in the Manokin River Basin on the Delmarva Peninsula. From March 2010 to June 2011, we conducted monthly sampling of 11 streams (4 tidal and 7 nontidal), 2 wastewater treatment plants, an agricultural drainage ditch, and groundwater underlying a cropped field. At each site, we measured urea‐N, DON, dissolved organic carbon (DOC), total dissolved nitrogen (TDN), NO₃^?‐N, and NH₄⁺‐N. In general, urea‐N comprised between 1% and 6% of TDN, with the highest urea‐N levels in drainage ditches (0.054 mg N/L) and wetland‐dominated streams (0.035–0.045 mg N/L). While urea‐N did not vary seasonally in tidal rivers, nontidal streams saw distinct urea‐N peaks in summer (0.038 mg N/L) that occurred several months after cropland fertilization in spring. Notably, the proportion of wetlands explained 78% of the variance in baseflow urea‐N levels across the Manokin watershed. In wetland‐dominated basins, we found urea‐N was positively related to water temperature and negatively related to DOC:DON ratios, indicating short‐term urea‐N dynamics at baseflow were more likely influenced by instream and wetland‐driven processes than by recent agricultural urea‐N inputs. Findings demonstrate important controls of wetlands on baseflow urea‐N concentrations in mixed land‐use basins.     

Potential Impacts of Highway Median Barriers on Wildlife: State of the Practice and Gap Analysis

Median barriers separate lanes of traffic moving in opposite directions on multilane highways. Such traffic safety devices can reduce head-on collisions but also have the potential to reduce landscape permeability by impeding wildlife movements across highways. Median barriers may also increase the risk of wildlife–vehicle collisions if an animal becomes trapped or confused amid barriers searching for a place to cross. A 2002 Transportation Research Board report highlighted the need to better understand the potential impacts of highway median barriers on wildlife. This lack of information can cause significant project delays and increase transportation project costs. This study represents the first attempt in North America to bring together information about highway median and roadside barriers and wildlife and provide preliminary guidelines to balance the needs of motorist safety and wildlife movements.     

Effect of Classification Procedure on the Performance of Numerically Defined Ecological Regions

Ecological regionalizations define geographic regions exhibiting relative homogeneity in ecological (i.e., environmental and biotic) characteristics. Multivariate clustering methods have been used to define ecological regions based on subjectively chosen environmental variables. We developed and tested three procedures for defining ecological regions based on spatial modeling of a multivariate target pattern that is represented by compositional dissimilarities between locations (e.g., taxonomic dissimilarities). The procedures use a “training dataset” representing the target pattern and models this as a function of environmental variables. The model is then extrapolated to the entire domain of interest. Environmental data for our analysis were drawn from a 400 m grid covering all of Switzerland and consisted of 12 variables describing climate, topography and lithology. Our target patterns comprised land cover composition of each grid cell that was derived from interpretation of aerial photographs. For Regionalization 1 we used conventional cluster analysis of the environmental variables to define 60 hierarchically organized levels comprising from 5 to 300 regions. Regionalization 1 provided a base-case for comparison with the model-based regionalizations. Regionalization 2, 3 and 4 also comprised 60 hierarchically organized levels and were derived by modeling land cover composition for 4000 randomly selected “training” cells. Regionalization 2 was based on cluster analysis of environmental variables that were transformed based on a Generalized Dissimilarity Model (GDM). Regionalization 3 and 4 were defined by clustering the training cells based on their land cover composition followed by predictive modeling of the distribution of the land cover clusters using Classification and Regression Tree (CART) and Random Forest (RF) models. Independent test data (i.e. not used to train the models) were used to test the discrimination of land cover composition at all hierarchical levels of the regionalizations using the classification strength (CS) statistic. CS for all the model-based regionalizations was significantly higher than for Regionalization 1. Regionalization 3 and 4 performed significantly better than Regionalization 2 at finer hierarchical levels (many regions) and Regionalization 4 performed significantly better than Regionalization 3 for coarse levels of detail (few regions). Compositional modeling can significantly increase the performance of numerically defined ecological regionalizations. CART and RF-based models appear to produce stronger regionalizations because discriminating variables are able to change at each hierarchic level.     

Body size preferences in the pot-bellied seahorse <Emphasis Type="Italic">Hippocampus abdominalis:</Emphasis> choosy males and indiscriminate females

Male seahorses (genus Hippocampus) provide all post-fertilization parental care, yet despite high levels of paternal investment, these species have long been thought to have conventional sex roles, with female mate choice and male–male competition. Recent studies of the pot-bellied seahorse (Hippocampus abdominalis) have shown that sex-role reversal occurs in high-density female-biased populations, indicating that male mating preferences may lead to sexual selection on females in this species. Egg size, egg number, and offspring size all correlate positively with female body size in Hippocampus, and by choosing large mating partners, male seahorses may increase their reproductive success. While male brood size is also positively correlated with body size, small H. abdominalis males can carry exceptionally large broods, suggesting that the fecundity benefits of female preference for large partners may be limited. We investigated the importance of body size in reproductive decisions of H. abdominalis, presenting focal individuals of both sexes with potential mating partners of different sizes. Mating preferences were quantified in terms of time spent courting each potential partner. Male seahorses were highly active throughout the mate-choice trials and showed a clear behavioral preference for large partners, while females showed significantly lower levels of activity and equivocal mating preferences. The strong male preferences for large females demonstrated here suggest that sexual selection may act strongly on female body size in wild populations of H. abdominalis, consistent with predictions on the importance of female body size for reproductive output in this species. An erratum to this article can be found at     

The Silver Valley Lead Study:The Relationship between Childhood Blood Lead Levels and Environmental Exposure

This paper is directed to those persons concerned with the relationship between blood lead levels and environmental exposures to lead. Information presented in this paper represents one of the largest collections of epidemiologica! data relating blood lead levels to environmental exposures. The observed annual average ambient air lead concentration ranged from approximately 0.5 ng Pb/m³ to 23 ng Pb/m³, while lead in soil ranged from 50-24,600 ppm. Blood lead levels of children (ages 1-9 years) are related to a host of environmental variables via regression techniques. Blood lead levels were found to be most influenced by five variables. These variables are: ambient air lead, soil lead, age of the child, dustiness of the home, and occupational status of the parents. It is concluded, based upon the results of this study, that any environmental control strategy should address both the air and the soil. Soil levels in excess of 1000 ppm lead as well as air lead levels greater than 2 jug Pb/m³, 30 day average, were found to be unacceptable.