Coastal protected area management and multi-tiered governance: the Cape Cod Model

Policy tools that allow for the coordination of various authorities at different levels of government are important for coastal protected areas. Frequently, multiple authorities manage these areas with varied and sometimes conflicting goals. This study examines a regulatory model implemented on the Cape Cod National Seashore in the US that uses federally-approved zoning to regulate private uses for protection of natural coastal resources. Local authorities implement the zoning which is designed to support national resource protection goals making this a prime model of cross-level governance for conservation. I use case study analysis to evaluate the program’s effectiveness by focusing on implementation and compliance in the context of multi-jurisdictional (i.e., national to local) relations. The analysis and subsequent discussion highlight the difficulties associated with implementation of intergovernmental mandates. Also, theoretical perspectives on compliance give insights about the implementation challenges of this model. The model’s limitations have implications for policymakers considering similar schemes implemented by split and hierarchical authorities with different, and possibly conflicting, coastal management goals.

Fiat boundaries: some implications for interpretation,decision-support,and multi-temporal analysis

Polygon-based thematic maps can be composed of boundaries that exist by definition—i.e., bona fide boundaries—or those that exist relative to a specific interpretation of a spatial phenomenon—i.e., fiat boundaries. The construction of maps composed of fiat boundaries is usually based on a subjective interpretive methodology that is affected by the data used to construct the map and the minimum mapping unit employed. That fiat boundaries are not the same as bona fide boundaries affects their use in computer-based spatial decision support tools. This is discussed both in terms of an analysis conducted at one specific moment, and in respect to increasingly common multi-temporal analysis.

Distribution and abundance of penaeid shrimps in a hypersaline lagoon in northwestern Mexico,emphasizing the brown shrimp <Emphasis Type="Italic">Farfantepenaeus californiensis</Emphasis> life cycle

Life histories of penaeid shrimp have been classified according to the preferred habitats of postlarval, juvenile, and adult stages, ranging from exclusively estuarine to exclusively offshore waters. Brown shrimp Farfantepenaeus californiensis migrate to an offshore habitat at the juvenile stage or even a smaller body size. This paper presents results of monthly samplings from 24 stations over 1 year in the Agiabampo Lagoon complex, a hypersaline lagoon in northwestern Mexico. Five species of penaeid shrimp were identified, with brown shrimp the most abundant during the year of sampling. Results suggest that residency of brown shrimp inside this lagoon is longer than reported in previous studies. An interaction between length and environmental variables (near-surface temperature, salinity, and rainfall) appear to be cues concerning migration.

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Testing homogeneity of capture probabilities of animals in a removal experiment

Consider the removal experiment used to estimate population sizes. Statistical methods towards testing the homogeneity of capture probabilities of animals, including a graphical diagnostic and a formal test, are presented and illustrated by real biological examples. Simulation is used to assess the test and compare it with the χ² test.

A note on non-parametric and semi-parametric modeling of wildfire hazard in Los Angeles County, California

This paper explores the use of, and problems that arise in, kernel smoothing and parametric estimation of the relationships between wildfire incidence and various meteorological variables. Such relationships may be treated as components in separable point process models for wildfire activity. The resulting models can be used for comparative purposes in order to assess the predictive performance of the Burning Index.

Multi-parameter renewal processes and simulation of forest fires

The concept of the renewal property is extended to processes indexed by a multidimensional time parameter. The definition given includes not only partial sum processes, but also Poisson processes and many other point processes whose jump points are not totally ordered. Various properties of renewal processes are discussed. Renewal processes are proposed as a basis for modelling the spread of a forest fire under a prevailing wind.

Behavior of yellowfin (<Emphasis Type="Italic">Thunnus albacares</Emphasis>) and bigeye (<Emphasis Type="Italic">T. obesus</Emphasis>) tuna in a network of fish aggregating devices (FADs)

The influence of multiple anchored fish aggregating devices (FADs) on the spatial behavior of yellowfin (Thunnus albacares) and bigeye tuna (T. obesus) was investigated by equipping all thirteen FADs surrounding the island of Oahu (HI, USA) with automated sonic receivers (“listening stations”) and intra-peritoneally implanting individually coded acoustic transmitters in 45 yellowfin and 12 bigeye tuna. Thus, the FAD network became a multi-element passive observatory of the residence and movement characteristics of tuna within the array. Yellowfin tuna were detected within the FAD array for up to 150 days, while bigeye tuna were only observed up to a maximum of 10 days after tagging. Only eight yellowfin tuna (out of 45) and one bigeye tuna (out of 12) visited FADs other than their FAD of release. Those nine fish tended to visit nearest neighboring FADs and, in general, spent more time at their FAD of release than at the others. Fish visiting the same FAD several times or visiting other FADs tended to stay longer in the FAD network. A majority of tagged fish exhibited some synchronicity when departing the FADs but not all tagged fish departed a FAD at the same time: small groups of tagged fish left together while others remained. We hypothesize that tuna (at an individual or collective level) consider local conditions around any given FAD to be representative of the environment on a larger scale (e.g., the entire island) and when those conditions become unfavorable the tuna move to a completely different area. Thus, while the anchored FADs surrounding the island of Oahu might concentrate fish and make them more vulnerable to fishing, at a meso-scale they might not entrain fish longer than if there were no (or very few) FADs in the area. At the existing FAD density, the ‘island effect’ is more likely to be responsible for the general presence of fish around the island than the FADs. We recommend further investigation of this hypothesis.

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Seals at sea: modelling seal distribution in the German bight based on aerial survey data

The Wadden Sea is an important habitat for harbour seals and grey seals. They regularly haul-out on sandbanks and islands along the coast. Comparably little is known about the time seals spend at sea and how they use the remainder of the North Sea. Yet, human activity in offshore waters is increasing and information on seal distribution in the North Sea is crucial for conservation and management. Aerial line transect surveys were conducted in the German bight from 2002 to 2007 to investigate the distribution and abundance of marine mammals. Distance sampling methodology was combined with density surface modelling for a spatially explicit analysis of seal distribution in the German North Sea. Depth and distance to coast were found to be relevant predictor variables for seal density. Density surface modelling allowed for a depiction of seal distribution in the study area as well as an abundance estimate. This is the first study to use aerial survey data to develop a density surface model (DSM) for a spatially explicit distribution estimate of seals at sea.

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A stochastic forest fire growth model

We consider a stochastic fire growth model, with the aim of predicting the behaviour of large forest fires. Such a model can describe not only average growth, but also the variability of the growth. Implementing such a model in a computing environment allows one to obtain probability contour plots, burn size distributions, and distributions of time to specified events. Such a model also allows the incorporation of a stochastic spotting mechanism.

Maximum likelihood fitting of the Poisson lognormal distribution

In this paper some properties and analytic expressions regarding the Poisson lognormal distribution such as moments, maximum likelihood function and related derivatives are discussed. The author provides a sharp approximation of the integrals related to the Poisson lognormal probabilities and analyzes the choice of the initial values in the fitting procedure. Based on these he describes a new procedure for carrying out the maximum likelihood fitting of the truncated Poisson lognormal distribution. The method and results are illustrated on real data. The computer program for calculations is freely available.

Improved strategies for coverage estimation by using replicated line-intercept sampling

Coverage, i.e., the area covered by the target attribute in the study region, is a key parameter in many surveys. Coverage estimation is usually performed by adopting a replicated protocol based on line-intercept sampling coupled with a suitable linear homogeneous estimator. Since coverage is a parameter which may be interestingly represented as the integral of a suitable function, improved Monte Carlo strategies for implementing the replicated protocol are introduced in order to achieve estimators with small variance rates. In addition, new specific theoretical results on Monte Carlo integration methods are given to deal with the integrand functions arising in the special coverage estimation setting.

A note on data augmentation and iterative simulation for survival rate estimation

In this paper we examine the use of data augmentation techniques for simplifying iterative simulation in the context of both Bayesian and classical statistical inference for survival rate estimation. We examine two distinct model families common in population ecology to illustrate our ideas, ring-recovery models and capture–recapture models, and we present the computational advantage of this approach. We discuss also the fact that problems associated with identifiability in the classical framework can be overcome using data augmentation, but highlight the dangers in doing so under both inferential paradigms.

Determining the optimum number of increments in composite sampling

Composite sampling can be more cost effective than simple random sampling. This paper considers how to determine the optimum number of increments to use in composite sampling. Composite sampling terminology and theory are outlined and a method is developed which accounts for different sources of variation in compositing and data analysis. This method is used to define and understand the process of determining the optimum number of increments that should be used in forming a composite. The blending variance is shown to have a smaller range of possible values than previously reported when estimating the number of increments in a composite sample. Accounting for differing levels of the blending variance significantly affects the estimated number of increments.

Confidence limits on one-stage model parameters in benchmark risk assessment

In modern environmental risk analysis, inferences are often desired on those low dose levels at which a fixed benchmark risk is achieved. In this paper, we study the use of confidence limits on parameters from a simple one-stage model of risk historically popular in benchmark analysis with quantal data. Based on these confidence bounds, we present methods for deriving upper confidence limits on extra risk and lower bounds on the benchmark dose. The methods are seen to extend automatically to the case where simultaneous inferences are desired at multiple doses. Monte Carlo evaluations explore characteristics of the parameter estimates and the confidence limits under this setting.

“Protecting coastal zones in the Mediterranean: an economic and regulatory analysis”

This paper examines existing measures taken to protect the coastal zones of the Mediterranean Sea and assesses their success. A summary of the main pressures facing this region is given, followed by an analysis of the legislation covering coastal zone development in ten countries: Algeria, Croatia, Egypt, France, Israel, Italy, Malta, Spain, Tunisia and Turkey. The paper finds that not all of these States have legislation specifically covering coastal zones, but there is concern in all areas that existing legislation is not working. The costs and benefits of controlling coastal development are examined. Firstly, a literature review of valuation studies identifies a range of values placed on the developed and undeveloped coastline for both users and local property owners. These values were then used in a model to evaluate policy options to control development of a stretch of coastline. The model indicates that a stricter control regime of coastal development may provide significant benefits.

Hierarchical spatial point process analysis for a plant community with high biodiversity

A complex multivariate spatial point pattern of a plant community with high biodiversity is modelled using a hierarchical multivariate point process model. In the model, interactions between plants with different post-fire regeneration strategies are of key interest. We consider initially a maximum likelihood approach to inference where problems arise due to unknown interaction radii for the plants. We next demonstrate that a Bayesian approach provides a flexible framework for incorporating prior information concerning the interaction radii. From an ecological perspective, we are able both to confirm existing knowledge on species’ interactions and to generate new biological questions and hypotheses on species’ interactions.

Trap mortality in mark-recapture studies

When animals die in traps in a mark-recapture study, straightforward likelihood inferences are possible in a class of models. The class includes M₀, M_t, and M_b as reported by White et al. (Los Alamos National Laboratory, LA-8787-NERP, pp 235, 1982), those that do not involve heterogeneity. We include three Markov chain “persistence” models and show that they provide good fits in a trapping study of deer mice in the Cascade-Siskiyou National Monument of Southern Oregon where trapping mortality was high.

Rarefaction and nonrandom spatial dispersion patterns

Rarefaction estimates how many species are expected in a random sample of individuals from a larger collection and allows meaningful comparisons among collections of different sizes. It assumes random spatial dispersion. However, two common dispersion patterns, within-species clumping and segregation among species, can cause rarefaction to overestimate the species richness of a smaller continuous area. We use field studies and computer simulations to determine (1) how robust rarefaction is to nonrandom spatial dispersion and (2) whether simple measures of spatial autocorrelation can predict the bias in rarefaction estimates. Rarefaction does not estimate species richness accurately for many communities, especially at small sample sizes. Measures of spatial autocorrelation of the more abundant species do not reliably predict amount of bias. Survey sites should be standardized to equal-sized areas before sampling. When sites are of equal area but differ in number of individuals sampled, rarefaction can standardize collections. When communities are sampled from different-sized areas, the mean and confidence intervals of species accumulation curves allow more meaningful comparisons among sites. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.