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 pattern and persistence of historical fire boundaries in southern interior British Columbia

Recent ecosystem and fire management research aims to quantify, model and understand historical fire disturbances focusing on their frequency, size and distribution. Less attention, however, has been paid to fire boundaries and their location on the landscape. Our study presents a spatial method to quantify the location, pattern and persistence of historical fire boundaries using tree ring fire scar data in the lower Stein watershed (British Columbia). Data from Pinus ponderosa and Pseudotsuga menziesii collected in 35 one-hectare plots over a 412-hectare study area were analyzed for the period between 1879 and 1947 using local spatial statistics and boundary detection techniques. Results of the analysis using local spatial statistic Moran’s I showed significant clustering of boundaries near topographic breaks. To determine and test whether fire boundaries between plots were persistent, we used boundary detection methods and developed a spatially restricted randomization test. The results revealed that out of 86 possible boundary links, 8 were significantly persistent (P < 0.025) while another 8 were significantly rare (P < 0.025). These spatial methods can help determine the historical spatial configuration of persistent boundaries and can be used to maintain natural forest dynamics.

Comparing geographic boundaries in songbird demography data with vegetation boundaries: a new approach to evaluating habitat quality

To plan for the habitat needs of forest songbirds of conservation concern, managers need to understand how spatial heterogeneity in forest conditions influences habitat quality. I used difference boundary detection (wombling) and spatially constrained clustering to delineate boundaries in various combinations of four forest vegetation variables (understory height, understory density, percent deciduous vs. conifer understory, and percent canopy closure) in two Michigan northern hardwood forests. My goal was to identify vegetation boundaries that corresponded with boundaries in an understory-dependent songbird’s distribution, and with boundaries in demographic measures for this songbird that indicate habitat quality (e.g., occupancy by older vs. yearling males, reproductive success). Both forests were actively-managed, mature stands: The first site (78 ha) was heavily deer-browsed (HB), with many browse-resistant conifers in the understory, and the second (62 ha) was less-browsed (LB), with deciduous-dominated understory. I compared the vegetation difference and cluster boundaries to difference boundaries based on 6 years of distribution and demographic data for black-throated blue warblers (Dendroica caerulescens). At the HB site, warbler boundaries overlapped strongly with vegetation boundaries that included all four variables, and clustering effectively divided the habitat into areas with different warbler occupancy and demographic characteristics. At the LB site, warbler distribution showed high overlap with difference and cluster boundaries based on just the height and density of understory vegetation, and cluster boundaries again effectively partitioned the study area into sites that varied in habitat quality. Thus, geographic boundary analysis is likely to be a useful tool for identifying key vegetation variables for management, and for delineating clusters (habitat patches) within sites that capture differences in habitat quality.

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.

Geographic sampling of urban soils for contaminant mapping: how many samples and from where

Properly sampling soils and mapping soil contamination in urban environments requires that impacts of spatial autocorrelation be taken into account. As spatial autocorrelation increases in an urban landscape, the amount of duplicate information contained in georeferenced data also increases, whether an entire population or some type of random sample drawn from that population is being analyzed, resulting in conventional power and sample size calculation formulae yielding incorrect sample size numbers vis-à-vis model-based inference. Griffith (in Annals, Association of American Geographers, 95, 740–760, 2005) exploits spatial statistical model specifications to formulate equations for estimating the necessary sample size needed to obtain some predetermined level of precision for an analysis of georeferenced data when implementing a tessellation stratified random sampling design, labeling this approach model-informed, since a model of latent spatial autocorrelation is required. This paper addresses issues of efficiency associated with these model-based results. It summarizes findings from a data collection exercise (soil samples collected from across Syracuse, NY), as well as from a set of resampling and from a set of simulation experiments following experimental design principles spelled out by Overton and Stehman (in Communications in Statistics: Theory and Methods, 22, 2641–2660). Guidelines are suggested concerning appropriate sample size (i.e., how many) and sampling network (i.e., where).

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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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.

Sustainable coastal communities in the age of coastal storms: Reconceptualising coastal planning as ‘new’ naval architecture

Hurricanes devastated the Gulf coast of the USA in 2005. Hurricane Katrina, in particular, highlighted the compelling need to build more sustainable and hazard-resilient communities. Much can be learned from recovery efforts to rebuild the Gulf coast. Personal observations and interviews with planners, academics and others involved in recovery efforts inform this analysis, which focuses on New Orleans. A conceptual framework is developed and principles and operational imperatives outlined to guide action for building sustainable, hazard-resilient communities. Such communities will remain elusive unless ‘business as usual’ is confronted by a transformational process of developmental planning. Sustainable, hazard-resilient coastal communities are founded upon robust ‘critical infrastructure’ that is secured by planning and decision-making processes that enable coastal communities to build ‘layers of resilience’ to overcome ‘waves of adversity’. Planners need to take on a redefined role—as ‘new naval architects’—to design and build communities that are ‘sea-worthy’ in this age of coastal storms.

A Bayesian approach to multi-source forest area estimation

In efforts such as land use change monitoring, carbon budgeting, and forecasting ecological conditions and timber supply, there is increasing demand for regional and national data layers depicting forest cover. These data layers must permit small area estimates of forest area and, most importantly, provide associated error estimates. This paper presents a model-based approach for coupling mid-resolution satellite imagery with plot-based forest inventory data to produce estimates of probability of forest and associated error at the pixel-level. The proposed Bayesian hierarchical model provides access to each pixel’s posterior predictive distribution allowing for a highly flexible analysis of pixel and multi-pixel areas of interest. The paper presents a trial using multiple dates of Landsat imagery and USDA Forest Service Forest Inventory and Analysis plot data. The results describe the spatial dependence structure within the trial site, provide pixel and multi-pixel summaries of probability of forest land use, and explore discretization schemes of the posterior predictive distributions to forest and non-forest classes. Model prediction results of a holdout set analysis suggest the proposed model provides high classification accuracy, 88%, for the trial site.

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.

Prospective spatial prediction of infectious disease: experience of New York State (USA) with West Nile Virus and proposed directions for improved surveillance

Infectious disease surveillance has become an international top priority due to the perceived risk of bioterrorism. This is driving the improvement of real-time geo-spatial surveillance systems for monitoring disease indicators, which is expected to have many benefits beyond detecting a bioterror event. West Nile Virus surveillance in New York State (USA) is highlighted as a working system that uses dead American Crows (Corvus brachyrhynchos) to prospectively indicate viral activity prior to human onset. A cross-disciplinary review is then presented to argue that this system, and infectious disease surveillance in general, can be improved by complementing spatial cluster detection of an outcome variable with predictive “risk mapping” that incorporates spatiotemporal data on the environment, climate and human population through the flexible class of generalized linear mixed models.

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 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.

Resource distributions affect social learning on multiple timescales

We study how learning is shaped by foraging opportunities and self-organizing processes and how this impacts on the effects of “copying what neighbors eat” on multiple timescales. We use an individual-based model with a rich environment, where group foragers learn what to eat. We vary foraging opportunities by changing local variation in resources, studying copying in environments with pure patches, varied patches, and uniform distributed resources. We find that copying can help individuals explore the environment by sharing information, but this depends on how foraging opportunities shape the learning process. Copying has the greatest impact in varied patches, where local resource variation makes learning difficult, but local resource abundance makes copying easy. In contrast, copying is redundant or excessive in pure patches where learning is easy, and mostly ineffective in uniform environments where learning is difficult. Our results reveal that the mediation of copying behavior by individual experience is crucial for the impact of copying. Moreover, we find that the dynamics of social learning at short timescales shapes cultural phenomena. In fact, the integration of learning on short and long timescales generates cumulative cultural improvement in diet. Our results therefore provide insight into how and when such processes can arise. These insights need to be taken into account when considering behavioral patterns in nature.

Flexible hierarchical mark-recapture modeling for open populations using WinBUGS

Hierarchical mark-recapture models offer three advantages over classical mark-recapture models: (i) they allow expression of complicated models in terms of simple components; (ii) they provide a convenient way of modeling missing data and latent variables in a way that allows expression of relationships involving latent variables in the model; (iii) they provide a convenient way of introducing parsimony into models involving many nuisance parameters. Expressing models using the complete data likelihood we show how many of the standard mark-recapture models for open populations can be readily fitted using the software WinBUGS. We include examples that illustrate fitting the Cormack–Jolly–Seber model, multi-state and multi-event models, models including auxiliary data, and models including density dependence.

Replace the NOAEL and LOAEL with the BMDL01 and BMDL10

Although benchmark-dose methodology has existed for more than 20 years, benchmark doses (BMDs) still have not fully supplanted the no-observed-adverse-effect level (NOAEL) and lowest-observed-adverse-effect level (LOAEL) as points of departure from the experimental dose–response range for setting acceptable exposure levels of toxic substances. Among the issues involved in replacing the NOAEL (LOAEL) with a BMD are (1) which added risk level(s) above background risk should be targeted as benchmark responses (BMRs), (2) whether to apply the BMD methodology to both carcinogenic and noncarcinogenic toxic effects, and (3) how to model continuous health effects that aren’t observed in a natural risk-based context like dichotomous health effects. This paper addresses these issues and recommends specific BMDs to replace the NOAEL and LOAEL.

Accounting for rate instability and spatial patterns in the boundary analysis of cancer mortality maps

Boundary analysis of cancer maps may highlight areas where causative exposures change through geographic space, the presence of local populations with distinct cancer incidences, or the impact of different cancer control methods. Too often, such analysis ignores the spatial pattern of incidence or mortality rates and overlooks the fact that rates computed from sparsely populated geographic entities can be very unreliable. This paper proposes a new methodology that accounts for the uncertainty and spatial correlation of rate data in the detection of significant edges between adjacent entities or polygons. Poisson kriging is first used to estimate the risk value and the associated standard error within each polygon, accounting for the population size and the risk semivariogram computed from raw rates. The boundary statistic is then defined as half the absolute difference between kriged risks. Its reference distribution, under the null hypothesis of no boundary, is derived through the generation of multiple realizations of the spatial distribution of cancer risk values. This paper presents three types of neutral models generated using methods of increasing complexity: the common random shuffle of estimated risk values, a spatial re-ordering of these risks, or p-field simulation that accounts for the population size within each polygon. The approach is illustrated using age-adjusted pancreatic cancer mortality rates for white females in 295 US counties of the Northeast (1970–1994). Simulation studies demonstrate that Poisson kriging yields more accurate estimates of the cancer risk and how its value changes between polygons (i.e., boundary statistic), relatively to the use of raw rates or local empirical Bayes smoother. When used in conjunction with spatial neutral models generated by p-field simulation, the boundary analysis based on Poisson kriging estimates minimizes the proportion of type I errors (i.e., edges wrongly declared significant) while the frequency of these errors is predicted well by the p-value of the statistical test.

The theoretical value of encounters with parasitized hosts for parasitoids

A female parasitoid searching for hosts in a patch experiences a diminishing encounter rate with unparasitized and thus suitable hosts. To use the available time most efficiently, it constantly has to decide whether to stay in the patch and continue to search for hosts or to search for and travel to another patch in the habitat. Several informational cues can be used to optimize the searching success. Theoretically, encounters with unparasitized hosts should lead to a prolonged search in a given patch if hosts are distributed contagiously. The results of empirical studies strongly support this hypothesis. However, it has, to date, not been investigated theoretically whether encounters with already parasitized hosts (which usually entail time costs) provide a parasitoid with valuable information for the optimization of its search in depletable patches, although the empirical studies concerning this question so far have produced ambiguous results. Building on recent advances in Bayesian foraging strategies, we approached this problem by modeling a priori searching strategies (which differ in the amount of information considered) and then testing them in computer simulations. By comparing the strategies, we were able to determine whether and how encounters with already parasitized hosts can yield information that can be used to enhance a parasitoid’s searching success.

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.