Genus-specific associations of marine sponges with group I crenarchaeotes

Sponges have rich and diverse associated microbial communities, which may have important functions in their metabolism. A survey of the archaeal communities of 23 poriferan species, focusing on the family Axinellidae, was conducted over the period 2002–2004 using 16S rDNA gene libraries created with archaeal-specific primers. The 28S rDNA sequences of the sponge hosts were also obtained. Of 23 species, 19 showed evidence of archaeal communities from group C1a (marine group I; Crenarchaeota), with three of these also showing evidence of Archaea from group E2 (marine group II; Euryarchaeota). Within the Crenarchaeota, two strongly supported sponge-specific clades were identified corresponding to the sponge family Axinellidae, and a novel sponge clade denoted clade C. These findings suggest that these archaea have evolved closely with their sponge hosts and are likely to play an important role in their metabolism.

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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.

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.

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.

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.

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.

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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Some comments on design-based line-intersect sampling with segmented transects

Line-intersect sampling based on segmented transects is adopted in many forest inventories to quantify important ecological indicators such as coarse woody debris attributes. By assuming a design-based approach, Affleck, Gregoire and Valentine (2005, Environ Ecol Stat 12:139–154) have recently proposed a sampling protocol for this line-intersect setting and have suggested an estimation method based on linear homogeneous estimators. However, their proposal does not encompass the estimation procedure currently adopted in some national forest inventories. Hence, the present paper aims to introduce a unifying perspective for both methods. Moreover, it is shown that the two procedures give rise to coincident estimators for almost all the usual field applications. Finally, some strategies for efficient segmented-transect replications are considered.

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.

Patch leaving decision rules in parasitoids: do they use sequential decisional sampling?

Within the framework of optimal foraging theory, models assume that parasitoid insects are able to evaluate the quality of the patch in which they are currently searching for hosts and the travel time between patches. They can adjust their residence time in consequence. Simple and more realistic decision mechanisms that induce behavior compatible with the predictions of these models have been proposed for a number of species. Most of these decision mechanisms only take into account the presence of unparasitized hosts. Here, we studied the consequences for leaving patches containing different proportions of unparasitized and parasitized hosts. We support the hypothesis that parasitoids sample their environment and we propose a binomial sequential model, based on the type of host encountered (unparasitized or parasitized) instead of on the time spent in a patch, to explain the giving-up behavior of a parasitoid in a patch. A motivational incremental/decremental stochastic process is proposed to explain a possible mechanism of the apparent sampling scheme followed by the insect. The empirical data support the hypothesis of a sequential, decisional, binomial sampling scheme performed with a limited memory. This memory is, in fact, more an effect of habituation than the "true memory" of the parasitoid. The theoretical model was applied to real data obtained with an encyrtid parasitoid. These data were also compared to realizations of the incremental/decremental process.     

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.

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.

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.

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.

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.

Spatial assessment of sea level rise on Martinique’s coastal zone and analysis of planning frameworks for adaptation

Accelerated sea level rise and hurricanes are increasingly influencing human coastal activities. With respect to the projected continuation of accelerated sea level rise and global warming one must count with additional expenses for adaptation strategies along the coasts. On the mountainous island Martinique the majority of settlements are situated along the coast almost at sea level. But potential rises in sea level and its impacts are not addressed in coastal management, even though saltwater intrusion and coastal erosion with increasing offshore loss of sediment are locally already a severe problem. At a sea level rise of 50 cm, one fourth of Martinique’s coastline will be affected by erosion and one fifth of the islands surface will have high probability to get flooded during coastal hazards. This is a growth of 5% of the impact area in comparison to present conditions. This article analyses potential adaptation strategies and argues that the development of a coastal zone management plan considering sea level rise and its impact area is of utmost importance. Empirical assessment models in combination with spatial analysis are useful in obtaining statements about coastal impacts concerning sea level rise. This paper sees itself as recommendation of action not only for Martinique.

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.

Fighting fire with fire: estimating the efficacy of wildfire mitigation programs using propensity scores

This paper examines the effect wildfire mitigation has on broad-scale wildfire behavior. Each year, hundreds of million of dollars are spent on fire suppression and fuels management applications, yet little is known, quantitatively, of the returns to these programs in terms of their impact on wildfire extent and intensity. This is especially true when considering that wildfire management influences and reacts to several, often times confounding factors, including socioeconomic characteristics, values at risk, heterogeneous landscapes, and climate. Due to the endogenous nature of suppression effort and fuels management intensity and placement with wildfire behavior, traditional regression models may prove inadequate. Instead, I examine the applicability of propensity score matching (PSM) techniques in modeling wildfire. This research makes several significant contributions including: (1) applying techniques developed in labor economics and in epidemiology to evaluate the effects of natural resource policies on landscapes, rather than on individuals; (2) providing a better understanding of the relationship between wildfire mitigation strategies and their influence on broad-scale wildfire patterns; (3) quantifying the returns to suppression and fuels management on wildfire behavior.

Social network analysis and valid Markov chain Monte Carlo tests of null models

Analyses of animal social networks derived from group-based associations often rely on randomisation methods developed in ecology (Manly, Ecology 76:1109–1115, 1995) and made available to the animal behaviour community through implementation of a pair-wise swapping algorithm by Bejder et al. (Anim Behav 56:719–725, 1998). We report a correctable flaw in this method and point the reader to a wider literature on the subject of null models in the ecology literature. We illustrate the importance of correcting the method using a toy network and use it to make a preliminary analysis of a network of associations among eagle rays.