Using revealed preferences to estimate the Value of Travel Time to recreation sites

The opportunity Value of Travel Time (VTT) is one of the most important elements of the total cost of recreation day-trips and arguably the most difficult to estimate. Most studies build upon the theoretical framework proposed by Becker (1965) by using a combination of revealed and stated preference data to estimate a value of time which is uniform in all activities and under all circumstances. This restriction is relaxed by DeSerpa's (1971) model which allows the value of saving time to be activity-specific. We present the first analysis which uses actual driving choices between open access and toll roads to estimate a VTT specific for recreation trips, thereby providing a value which conforms to both Becker's and DeSerpa's theoretical models. Using these findings we conduct a Monte Carlo simulation to identify generalizable results for subsequent valuation studies. Our results indicate that 3/4 of the wage rate provides a reasonable approximation of the average VTT for recreation trips, while the commonly implemented assumption of 1/3 of the wage rate generates downward biased results.     

Static modeling of dynamic recreation behavior: Implications for prediction and welfare estimation

This paper examines the consequences of using a static model of recreation trip-taking behavior when the underlying decision problem is dynamic. Specifically we examine the implications for trip forecasting and welfare estimation using a panel dataset of Lake Michigan salmon anglers for the 1996 and 1997 fishing seasons. We derive and estimate both a structural dynamic model using Bellman's equation, and a reduced-form static model with trip probability expressions mimicking those of the dynamic model. We illustrate an inherent identification problem in the reduced-form model that creates biased welfare estimates, and we discuss the general implications of this for the interpretation of preference parameters in static models. We then use both models to simulate trip taking behavior and show that although their in-sample trip forecasts are similar, their welfare estimates and out-of-sample forecasts are quite different.     

Null models reveal preferential sampling, spatial autocorrelation and overfitting in habitat suitability modelling

Nowadays, species are driven to extinction at a high rate. To reduce this rate it is important to delineate suitable habitats for these species in such a way that these areas can be suggested as conservation areas. The use of habitat suitability models (HSMs) can be of great importance for the delineation of such areas. In this study MaxEnt, a presence-only modelling technique, is used to develop HSMs for 223 nematode species of the Southern Bight of the North Sea. However, it is essential that these models are beyond discussion and they should be checked for potential errors. In this study we focused on two categories (1) errors which can be attributed to the database such as preferential sampling and spatial autocorrelation and (2) errors induced by the modelling technique such as overfitting, In order to quantify these adverse effects thousands of nulls models were created. The effect of preferential sampling (i.e. some areas where visited more frequenty than others) was investigated by comparing model outcomes based from null models sampling the actual sampling stations and null models sampling the entire mapping area (Raes and ter Steege, 2007). Overfitting is exposed by a fivefold cross-validation and the influence of spatial autocorrelation is assessed by separating test and training sets in space. Our results clearly show that all these effects are present: preferential sampling has a strong effect on the selection of non-random species models. Crossvalidation seems to have less influence on the model selection and spatial autocorrelation is also strongly present. It is clear from this study that predefined thresholds are not readily applicable to all datasets and additional tests are needed in model selection.     

Time constraints and multiple choice criteria in the sampling behaviour and mate choice of the fiddler crab,Uca annulipes

Active female sampling occurs in the fiddler crab Uca annulipes. Females sample the burrows of several males before remaining to mate in the burrow of the chosen partner. Females time larval release to coincide with the following nocturnal spring tide and must therefore leave sufficient time for embryonic development after mating. Here we show how this temporal constraint on search time affects female choosiness. We found that, at the start of the sampling period (when time constraints are minimal), females selectively sample the larger males in the population. Towards the end of the sampling period (when the temporal constraints increase the costs of sampling), females are less selective. Furthermore, we suggest that the number of males sampled (and other indices of ‘‘sampling effort’’) may not be reliable indicators of female choosiness and may not reflect the strength of female mating preferences under certain conditions. Burrow quality also emerged as an important criterion in final mate choice. Burrow structure potentially influences reproductive success, and mate acceptance based on burrow structure appears to involve a relatively invariant threshold criterion. Since there is no relationship between male size and burrow quality, females are using at least two independent criteria when choosing potential mates. We envisage mate choice as a two-stage process. First, females select which males to sample based on male size. They then decide whether or not to mate with a male based on burrow features. This sampling process explains how two unrelated variables can both predict male mating success. Received: 23 March 1995/Accepted after revision: 14 January 1996     

Colony choice in birds: models based on temporally invariant site quality

We propose two stochastic models to explain how birds choose colonies. In the resource choice model, birds settle at each site at a rate proportional to the total resources the site contains. In the reduced resource choice model, a smaller cohort of birds enters sites at a rate determined by the total resources at each, and the remaining individuals enter sites at a rate that is linearly proportional to the total number of birds already nesting at each site. Thus, a fraction of birds chooses sites based on the resources present, and the remainder are attracted to a site by the presence of other birds. Colony site quality is assumed not to vary between years. Both models result, on average, in an ideal free distribution of colony sizes if the birds' settlement rate is linearly related to the resources in a site, if resources are distributed equally among individuals within sites, and if individuals with equal resources have equal fitness. We applied these models to long-term data on colony sizes and site usage of cliff swallows in south-western Nebraska. A test of the resource choice model suggested that the swallow population as a whole did not choose sites based strictly on site quality or the total resources contained at each site. However, a test of the reduced resource choice model suggested that a smaller fraction of the individuals in each colony may have based their choice of site on local resource availability, with the remaining birds aggregating at those sites based on the number of birds already settled there. Tests of these models may provide insight into how individuals choose colony sites and why colonies vary in size.     

Multistate models for monitoring individual trees in permanent observation plots

This interdisciplinary research on forest ecosystems begins with some characteristics of ecosystems which are the basis for the derivation of statistical models for the development and vitality of trees. Several ecological problems which could be solved by longitudinal studies are mentioned. Statistical methods for the evaluation of the crowns of spruce trees (Picea abies Karst) in three permanent observation plots in Switzerland are described. In particular, the time-dependent proportional odds model and a transitional model are used. Through application of these multistate models the data give information on the dependence of an ordered categorical response variable on covariates characterizing the ecosystem. The response variable is observed through infrared aerial photographs. This monitoring system gives insight into the dynamic behaviour of the forest ecosystem. The need for more eco-systematically motivated statistical research using longitudinal studies is identified.