On the use of nearest neighbor contingency tables for testing spatial segregation

For two or more classes (or types) of points, nearest neighbor contingency tables (NNCTs) are constructed using nearest neighbor (NN) frequencies and are used in testing spatial segregation of the classes. Pielou’s test of independence, Dixon’s cell-specific, class-specific, and overall tests are the tests based on NNCTs (i.e., they are NNCT-tests). These tests are designed and intended for use under the null pattern of random labeling (RL) of completely mapped data. However, it has been shown that Pielou’s test is not appropriate for testing segregation against the RL pattern while Dixon’s tests are. In this article, we compare Pielou’s and Dixon’s NNCT-tests; introduce the one-sided versions of Pielou’s test; extend the use of NNCT-tests for testing complete spatial randomness (CSR) of points from two or more classes (which is called CSR independence, henceforth). We assess the finite sample performance of the tests by an extensive Monte Carlo simulation study and demonstrate that Dixon’s tests are also appropriate for testing CSR independence; but Pielou’s test and the corresponding one-sided versions are liberal for testing CSR independence or RL. Furthermore, we show that Pielou’s tests are only appropriate when the NNCT is based on a random sample of (base, NN) pairs. We also prove the consistency of the tests under their appropriate null hypotheses. Moreover, we investigate the edge (or boundary) effects on the NNCT-tests and compare the buffer zone and toroidal edge correction methods for these tests. We illustrate the tests on a real life and an artificial data set.     

Modelling asymmetrically distributed circular data using the wrapped skew-normal distribution

We consider problems of inference for the wrapped skew-normal distribution on the circle. A centered parametrization of the distribution is introduced, and simulation used to compare the performance of method of moments and maximum likelihood estimation for its parameters. Maximum likelihood estimation is shown, in general, to be superior. The operating characteristics of two moment based tests, for wrapped normal and wrapped half-normal parent populations, respectively, are also explored. The former test is easy to apply, maintains the nominal significance level well and is generally highly powerful. The latter test does not hold the nominal significance level so well, although it is very powerful against negatively skew alternatives. Likelihood based tests for the two distributions are also discussed. A real data set from the ornithological literature is used to illustrate the application of the developed methodology and its extension to finite mixture modelling. Received: September 2003/ Revised: April 2005     

Nearest neighbor methods for testing reflexivity

Nearest neighbor (NN) methods are widely employed for drawing inferences about spatial point patterns of two or more classes. We introduce a method for testing reflexivity in the NN structure (i.e., NN reflexivity) based on a contingency table which will be called reflexivity contingency table (RCT) henceforth. The RCT is based on the NN relationships among the data points and was used for testing niche specificity in literature, but we demonstrate that it is actually more appropriate for testing the NN reflexivity pattern. We derive the asymptotic distribution of the entries of the RCT under random labeling and introduce tests of reflexivity based on these entries. We also consider Pielou’s approach on RCT and show that it is not appropriate for completely mapped spatial data. We determine the appropriate null hypotheses and the underlying conditions/assumptions required for all tests considered. We investigate the finite sample performance of the tests in terms of empirical size and power by extensive Monte Carlo simulations and illustrate the methods on two real-life ecological data sets.     

How does the spatial structure of habitat loss affect the eco-epidemic dynamics?

Habitat loss is considered as one of the primary causes of species extinction, especially for a species that also suffers from an epidemic disease. Little attention has been paid to the combined effect of habitat loss and epidemic transmission on the species spatiotemporal dynamics. Here, a spatial model of the parasite–host/prey–predator eco-epidemiological system with habitat loss was studied. Habitat patches in the model, instead of undergoing a random loss, were spatially clustered by different degrees. Not only the quantity of habitat loss but also its clustering degree was shown to affect the equilibrium of the system. The infection rate and the probability of successful predation were keys to determine the spatial patterns of species. The epidemic disease is more likely to break out if only a small amount of suitable patches were lost. Counter-intuitively, infected preys are more sensitive to habitat loss than predators if the lost patches are highly clustered. This result is new to eco-epidemiology and implies a possibility of using spatial arrangement of suitable (or unsuitable) patches to control the spread of epidemics in the ecological system.     

Two neighborhood-free plot designs for adaptive sampling of forests

Adaptive cluster sampling (ACS) has the potential of being superior for sampling rare and geographically clustered populations. However, setting up an efficient ACS design is challenging. In this study, two adaptive plot designs are proposed as alternatives: one for fixed-area plot sampling and the other for relascope sampling (also known as variable radius plot sampling). Neither includes a neighborhood search which makes them much easier to execute. They do, however, include a conditional plot expansion: at a sample point where a predefined condition is satisfied, sampling is extended to a predefined larger cluster-plot or a larger relascope plot. Design-unbiased estimators of population total and its variance are derived for each proposed design, and they are applied to ten artificial and one real tree position maps to estimate density (number of trees per ha) and basal area (the cross-sectional area of a tree stem at breast height) per hectare. The performances—in terms of relative standard error (SE%)—of the proposed designs and their non-adaptive alternatives are compared. The adaptive plot designs were superior for the clustered populations in all cases of equal sample sizes and in some cases of equal area of sample plots. However, the improvement depends on: (1) the plot size factor; (2) the critical value (the minimum number of trees triggering an expansion); (3) the subplot distance for the adapted cluster-plots, and (4) the spatial arrangement of the sampled population. For some spatial arrangements, the improvement is relatively small. The adaptive designs may be particularly attractive for sampling in rare and compactly clustered populations with critical value of 1, subplot distance equal to the diameter of initial circular plots, or plot size factor of 2.5 for an initial basal area factor of 2.     

A diagnostic test for autocorrelation in increment-averaged data with application to soil sampling

Motivated by the problem of detecting spatial autocorrelation in increment- averaged data from soil core samples, we use the Cholesky decomposition of the inverse of an autocovariance matrix to derive a parametric linear regression model for autocovariances. In the absence of autocorrelation, the off-diagonal terms in the lower triangular matrix from the Cholesky decomposition should be identically zero, and so the regression coefficients should be identically zero. The standard F-test of this hypothesis and two bootstrapped versions of the test are evaluated as autocorrelation diagnostics via simulation. Size is assessed for a variety of heteroskedastic null hypotheses. Power is evaluated against autocorrelated alternatives, including increment-averaged Ornstein-Uhlenbeck and Matérn processes. The bootstrapped tests maintain approximately the correct size and have good power against moderately autocorrelated alternatives. The methods are applied to data from a study of carbon sequestration in agricultural soils.     

抚育间伐对栓皮栎种群空间分布格局的影响

抚育间伐是一种重要的改善林木生长条件的经营措施,对林分结构和动态具有重要影响。为研究抚育间伐对林木种群空间结构与格局影响的内在机制,以间伐和未间伐的栓皮栎人工林为研究对象,通过设置2个100 m×100 m样地并进行每木定位和调查,在采用径级结构代替年龄结构方法将栓皮栎种群划分为幼树(2 cm≤DBH<5 cm)、小树(5 cm≤DBH<13 cm)和大树(DBH≥13 cm)3个生长阶段的基础上,分别采用Ripley’s K函数衍生的g(r)函数和双变量g12(r)函数,对栓皮栎种群空间分布点格局及不同生长阶段栓皮栎种群之间的关联性进行了研究。结果表明,间伐和未间伐样地栓皮栎种群空间分布点格局分别在0-16 m和0-33 m距离尺度内呈聚集分布,而分别在大于16 m和33 m距离尺度内呈随机分布;间伐和未间伐样地栓皮栎幼树、小树和大树的株数比分别为8?741?699和261?1134?683,且间伐样地栓皮栎幼树、小树和大树种群分别在0-14、1-16、0-6 m距离尺度内呈现均匀或聚集分布,而在其他距离尺度上表现为随机分布;栓皮栎幼树、小树和大树之间仅在间伐样地0-6 m距离尺度内呈现一定的相关性,而在未间伐样地更大的距离尺度内有显著的关联性,如栓皮栎幼树和大树之间在6-38 m距离尺度上呈显著正相关。因此,抚育间伐一定程度上使得栓皮栎种群在更大距离尺度上呈现出随机分布状态,并弱化了不同生长阶段的林木栓皮栎种群的关联性,这调整了栓皮栎种群空间竞争关系,有利于大径级林木个体的培育。该研究可以为开展抚育间伐对林木种群的影响的研究提供理论依据,也可以为制定科学合理的抚育技术措施提供参考。     

Is software sustainability considered in the CSR of software industry?

Software is a key factor in the functioning of today’s world. Software is supposed to have some characteristics such as: reliability, security, etc., but it is not at all easy to find energy efficiency considered as being one of software’s most important features. Aspects related to Green Software have begun to be considered vital and basic, due to pressure from a society which is becoming more and more aware of environmental problems. In this paper we want to explore whether software companies, responsible for developing software, are aligning their strategies with environmental concerns. To do so, we have checked the Corporate Social Responsibility (CSR) policies of the ten most important software companies and looked at these documents with reference to the UN’s sustainable development goals. A well-defined method for carrying out the analysis of the CSR policies led the authors to identify which of the sustainability actions proposed can be classified within any of the software sustainability dimensions. The analysis of these actions shows that sustainability is considered in most of the companies; nonetheless, most attention is currently devoted to hardware-based initiatives intended to reduce the carbon footprint of the hardware resources of the companies. In addition, green software initiatives are still not the priority, despite the influence of software on energy consumption, and in spite of its impact on the environment. Using the set of actions selected, a set of specific actions for software sustainability, to be included in the CSR of software companies, has been defined.     

Firm ownership structure impact on corporate social responsibility: evidence from austerity U.K.

Corporate social responsibility (CSR) has become an increasingly important sustainable development issue in U.K. The main contribution of this study is to examine how firm ownership structure impacts good corporate governance and CSR in U.K. during austerity conditions. Following the financial crisis of 2007–2008, the U.K. government introduced austerity conditions which impacted firm CSR activities. From the initial sample of more than 250 firms, 50 consistently remain listed on the FTSE4good index during 2008–2012 and are analysed. The definition of CSR distinguishes voluntary and mandatory CSR construct. Findings indicate Board ownership structure and satisfactory firm performance impact on the level of voluntary CSR. Board ownership results suggest increased institutional and non-CEO shareholdings support a higher level of voluntary CSR engagement, whilst increased CEO shareholdings lead to a lower level of investment in voluntary CSR. In terms of satisfactory firm performance, results suggest positive attainment discrepancy supports a higher level of voluntary CSR, whereas greater potential organisational slack leads to a lower level of voluntary CSR investment. Effective governance and voluntary CSR association is more pronounced under conditions of high attainment discrepancy and low organisational slack. The findings suggest implications for adapting firm decision-making latitude and government policy between austerity and prosperity conditions.     

Generating spatially optimized habitat in a trade‐off between social optimality and budget efficiency

Auctions have been proposed as alternatives to payments for environmental services when spatial interactions and costs are better known to landowners than to the conservation agency (asymmetric information). Recently, an auction scheme was proposed that delivers optimal conservation in the sense that social welfare is maximized. I examined the social welfare and the budget efficiency delivered by this scheme, where social welfare represents the difference between the monetized ecological benefit and the conservation cost incurred to the landowners and budget efficiency is defined as maximizing the ecological benefit for a given conservation budget. For the analysis, I considered a stylized landscape with land patches that can be used for agriculture or conservation. The ecological benefit was measured by an objective function that increases with increasing number and spatial aggregation of conserved land patches. I compared the social welfare and the budget efficiency of the auction scheme with an agglomeration payment, a policy scheme that considers spatial interactions and that was proposed recently. The auction delivered a higher level of social welfare than the agglomeration payment. However, the agglomeration payment was more efficient budgetarily than the auction, so the comparative performances of the 2 schemes depended on the chosen policy criterion–social welfare or budget efficiency. Both policy criteria are relevant for conservation. Which one should be chosen depends on the problem at hand, for example, whether social preferences should be taken into account in the decision of how much money to invest in conservation or whether the available conservation budget is strictly limited.     

Spatial clustering of habitat structure effects patterns of community composition and diversity

Natural ecosystems often show highly productive habitats that are clustered in space. Environmental disturbances are also often nonrandomly distributed in space and are either intrinsically linked to habitat quality or independent in occurrence. Theoretical studies predict that configuration and aggregation of habitat patch quality and disturbances can affect metacommunity composition and diversity, but experimental evidence is largely lacking. In a metacommunity experiment, we tested the effects of spatially autocorrelated disturbance and spatial aggregation of patch quality on regional and local richness, among-community dissimilarity, and community composition. We found that spatial aggregation of patch quality generally increased among-community dissimilarity (based on two measures of beta diversity) of communities containing protozoa and rotifers in microcosms. There were significant interacting effects of landscape structure and location of disturbances on beta diversity, which depended in part on the specific beta diversity measures used. Effects of disturbance on composition and richness in aggregated landscapes were generally dependent on distance and connectivity among habitat patches of different types. Our results also show that effects of disturbances in single patches cannot directly be extrapolated to the landscape scale: the predictions may be correct when only species richness is considered, but important changes in beta diversity may be overlooked. There is a need for biodiversity and conservation studies to consider the spatial aggregation of habitat quality and disturbance, as well as connectivity among spatial aggregations.     

GIS and geostatistics: Essential partners for spatial analysis

Initially, geographical information systems (GIS) concentrated on two issues: automated map making, and facilitating the comparison of data on thematic maps. The first required high quality graphics, vector data models and powerful data bases, the second is based on grid cells that can be manipulated by suites of mathematical operators collectively termed map algebra. Both kinds of GIS are widely available and are taught in many universities and technical colleges. After more than 20 years of development, most standard GIS provide both kinds of functionality and good quality graphic display, but until recently they have not included the methods of statistics and geostatistics as tools for spatial analysis. Recently, standard statistical packages have been linked to GIS for both exploratory data analysis and statistical analysis and hypothesis testing. Standard statistical packages include methods for the analysis of random samples of cases or objects that are not necessarily co-located in space—if the results of statistical analysis display a spatial pattern then that is because the underlying data also share that pattern. Geostatistics addresses the need to make predictions of sampled attributes (i.e., maps) at unsampled locations from sparse, often expensive data. To make up for lack of hard data geostatistics has concentrated on the development of powerful methods based on stochastic theory. Though there have been recent moves to incorporate ancillary data in geostatistical analyses, insufficient attention has been paid to using modern methods of data display for the visualization of results. GIS can serve geostatistics by aiding geo-registration of data, facilitating spatial exploratory data analysis, providing a spatial context for interpolation and conditional simulation, as well as providing easy-to-use and effective tools for data display and visualization. The value of geostatistics for GIS lies in the provision of reliable interpolation methods with known errors, methods of upscaling and generalization, and for supplying multiple realizations of spatial patterns that can be used in environmental modeling. These stochastic methods are improving understanding of how errors in models of spatial processes accrue from errors in data or incompleteness in the structure of the models. New developments in GIS, based on ideas taken from map algebra, cellular automata and image analysis are providing high level programming languages for modeling dynamic processes such as erosion or the development of alluvial fans and deltas. Research has demonstrated that these models need stochastic inputs to yield realistic results. Non-stochastic tools such as fuzzy subsets have been shown to be useful for spatial analysis when probabilistic approaches are inappropriate or impossible. The conclusion is that in spite of differences in history and approach, the linkage of GIS, statistics and geostatistics provides a powerful, and complementary suite of tools for spatial analysis in the agricultural, earth and environmental sciences.     

Parasitism and a shortage of refuges jointly mediate the strength of density dependence in a reef fish

Various predator-prey, host-pathogen, and competitive interactions can combine to cause density dependence in population growth. Despite this possibility, most empirical tests for density-dependent interactions have focused on single mechanisms. Here we tested the hypothesis that two mechanisms of density dependence, parasitism and a shortage of refuges, jointly influence the strength of density-dependent mortality. We used mark-recapture analysis to estimate mortality of the host species, the bridled goby (Coryphopterus glaucofraenum). Sixty-three marked gobies were infected with a copepod gill parasite (Pharodes tortugensis), and 188 were uninfected. We used the spatial scale at which gobies were clustered naturally (approximately 4 m2) as an ecologically relevant neighborhood and measured goby density and the availability of refuges from predators within each goby's neighborhood. Goby survival generally declined with increasing density, and this decline was steeper for gobies with access to few refuges than for gobies in neighborhoods where refuges were common. The negative effects of high density and refuge shortage were also more severe for parasitized gobies than for gobies free of parasites. This parasite has characteristics typical of emerging diseases and appears to have altered the strength of a preexisting density-dependent interaction.     

Scaling range sizes to threats for robust predictions of risks to biodiversity

Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range‐size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in red‐list assessments for decades, appropriate spatial scales of AOO for predicting risks of species’ extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scale‐sensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81<|r|<0.98) and performed optimally when measured with grid cells 0.1–1.0 times the largest plausible area threatened by an event. Contrary to previous assertions, estimates of AOO at these relatively coarse scales were better predictors of risk than finer‐scale estimates of AOO (e.g., when measurement cells are <1% of the area of the largest threat). The optimal scale depended on the spatial scales of threats more than the shape or size of biotic distributions. Although we found appreciable potential for grid‐measurement errors, current IUCN guidelines for estimating AOO neutralize geometric uncertainty and incorporate effective scaling procedures for assessing risks posed by landscape‐scale threats to species and ecosystems.     

Hunter Reporting of Catch per Unit Effort as a Monitoring Tool in a Bushmeat‐Harvesting System

Abstract: Growing threats to biodiversity in the tropics mean there is an increasing need for effective monitoring that balances scientific rigor with practical feasibility. Alternatives to professional techniques are emerging that are based on the involvement of local people. Such locally based monitoring methods may be more sustainable over time, allow greater spatial coverage and quicker management decisions, lead to increased compliance, and help encourage attitude shifts toward more environmentally sustainable practices. Nevertheless, few studies have yet compared the findings or cost‐effectiveness of locally based methods with professional techniques or investigated the power of locally based methods to detect trends. We gathered data on bushmeat‐hunting catch and effort using a professional technique (accompanying hunters on hunting trips) and two locally based methods in which data were collected by hunters (hunting camp diaries and weekly hunter interviews) in a 15‐month study in Equatorial Guinea. Catch and effort results from locally based methods were strongly correlated with those of the professional technique and the spatial locations of hunting trips reported in the locally based methods accurately reflected those recorded with the professional technique. We used power simulations of catch and effort data to show that locally based methods can reliably detect meaningful levels of change (20% change with 80% power at significance level [α]= 0.05) in multispecies catch per unit effort. Locally based methods were the most cost‐effective for monitoring. Hunter interviews collected catch and effort data on 240% more hunts per person hour and 94% more hunts per unit cost, spent on monitoring, than the professional technique. Our results suggest that locally based monitoring can offer an accurate, cost‐effective, and sufficiently powerful method to monitor the status of natural resources. To establish such a system in Equatorial Guinea, the current lack of national and local capacity for monitoring and management must be addressed.     

Spatial uncertainty analysis of population models

This paper describes an approach for conducting spatial uncertainty analysis of spatial population models, and illustrates the ecological consequences of spatial uncertainty for landscapes with different properties. Spatial population models typically simulate birth, death, and migration on an input map that describes habitat. Typically, only a single “reference” map is available, but we can imagine that a collection of other, slightly different, maps could be drawn to represent a particular species’ habitat. As a first approximation, our approach assumes that spatial uncertainty (i.e., the variation among values assigned to a location by such a collection of maps) is constrained by characteristics of the reference map, regardless of how the map was produced. Our approach produces lower levels of uncertainty than alternative methods used in landscape ecology because we condition our alternative landscapes on local properties of the reference map. Simulated spatial uncertainty was higher near the borders of patches. Consequently, average uncertainty was highest for reference maps with equal proportions of suitable and unsuitable habitat, and no spatial autocorrelation. We used two population viability models to evaluate the ecological consequences of spatial uncertainty for landscapes with different properties. Spatial uncertainty produced larger variation among predictions of a spatially explicit model than those of a spatially implicit model. Spatially explicit model predictions of final female population size varied most among landscapes with enough clustered habitat to allow persistence. In contrast, predictions of population growth rate varied most among landscapes with only enough clustered habitat to support a small population, i.e., near a spatially mediated extinction threshold. We conclude that spatial uncertainty has the greatest effect on persistence when the amount and arrangement of suitable habitat are such that habitat capacity is near the minimum required for persistence.     

The number of males in primate social groups: a comparative test of the socioecological model

As applied to polygynous mammals, the socioecological model assumes that environmental risks and resources determine the spatial and temporal distribution of females, which then sets male strategies for monopolizing fertile matings. The effects of female spatial distribution (i.e., female number) and temporal overlap (female mating synchrony) have been examined in comparative studies of primates, but the relative influence of these two factors on male monopolization potential (the number of males) remains unclear. One particular problem is that female synchrony is more difficult to estimate than female number. This paper uses multivariate statistical methods and three independent estimates of female synchrony to assess the roles of spatial and temporal effects in the context of a phylogenetically corrected dataset. These analyses are based on sensitivity analyses involving a total of four phylogenies, with two sets of branch length estimates for each tree, and one nonphylogenetic analysis in which species values are used (because male behavior may represent a facultative response to the distribution of females). The results show: (1) that breeding seasonality predicts male number (statistically significant in six out of nine sensitivity tests); (2) that expected female overlap, after controlling for female group size using residuals, also accounts for the number of males in primate groups (significant in eight out of nine tests), and (3) that actual estimates of female mating synchrony predict male number, again after correcting for female group size (significant in five out of nine tests). Nonsignificant results are in the predicted direction, and female group size is significant in all statistical tests. These analyses therefore demonstrate an independent influence of female temporal overlap on male monopolization strategies in mammalian social systems. Received: 24 July 1998 / Received in revised form: 5 February 1999 / Accepted: 7 February 1999     

A comparative decision-making model for sustainable end-of-life vehicle management alternative selection using AHP and extent analysis method on fuzzy AHP

Environmental protection and sustainable development are getting increasing attention in the automobile industries. Environmental consciousness has increased, and sustainability has become an important requirement for the management of end-of-life vehicles (ELVs). The proper management of ELVs can bring improved sustainability performance for any society or organisation. There are several alternative options for ELVs management such as reuse, repair, reconditioning, remanufacturing and recycling. Environmental legislation is forcing original equipment manufacturers (OEMs) to manage their products at the end of their life cycle to reduce their potential environmental impact. An appropriate selection model for sustainable ELV management alternatives in the dynamic, competitive and regulatory environment can enable a firm to satisfy economic, environmental, social and technological requirements. This study proposes an integrated model to select the dimensions and criteria for evaluating sustainable alternatives for the proper management of ELVs. First, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to select the most important dimensions and criteria for sustainable alternative selection. Next, a hierarchy has been constructed to develop a systematic technique to solve the alternatives selection problem. An analytic hierarchy process (AHP) and extent analysis method on the fuzzy AHP (FEAHP) model have been used based on the hierarchy to evaluate the most suitable alternatives from the sustainability point of view. The strength of the proposed model is that it compares the results of both methods, the imprecision of experts’ opinions is considered in the evaluation process and the model is easy to use.     

Comparing spatial conservation prioritization methods with site- versus spatial dependency-based connectivity

Larval dispersal is an important component of marine reserve networks. Two conceptually different approaches to incorporate dispersal connectivity into spatial planning of these networks exist, and it is an open question as to when either is most appropriate. Candidate reserve sites can be selected individually based on local properties of connectivity or on a spatial dependency-based approach of selecting clusters of strongly connected habitat patches. The first acts on individual sites, whereas the second acts on linked pairs of sites. We used a combination of larval dispersal simulations representing different seascapes and case studies of biophysical larval dispersal models in the Coral Triangle region and the province of Southeast Sulawesi, Indonesia, to compare the performance of these 2 methods in the spatial planning software Marxan. We explored the reserve design performance implications of different dispersal distances and patterns based on the equilibrium settlement of larvae in protected and unprotected areas. We further assessed different assumptions about metapopulation contributions from unprotected areas, including the case of 100% depletion and more moderate scenarios. The spatial dependency method was suitable when dispersal was limited, a high proportion of the area of interest was substantially degraded, or the target amount of habitat protected was low. Conversely, when subpopulations were well connected, the 100% depletion was relaxed, or more habitat was protected, protecting individual sites with high scores in metrics of connectivity was a better strategy. Spatial dependency methods generally produced more spatially clustered solutions with more benefits inside than outside reserves compared with site-based methods. Therefore, spatial dependency methods potentially provide better results for ecological persistence objectives over enhancing fisheries objectives, and vice versa. Different spatial prioritization methods of using connectivity are appropriate for different contexts, depending on dispersal characteristics, unprotected area contributions, habitat protection targets, and specific management objectives. Comparación entre los métodos de priorización de la conservación espacial con sitio y la conectividad espacial basada en la dependencia     

Tests for a biased sex ratio when the data are clustered

In behavioral ecology the overall sex ratio in a population of birds is often tested to see if it differs from a 50/50 ratio. In recent publications the binomial test or the ² test are carried out although the sexes of chicks within the same nest may not be independent. The lack of independence occurs since female birds can adjust the sex ratio in an adaptive way as demonstrated in recent studies. In order to take dependence into consideration the Wilcoxon signed rank test based on the within-brood differences between the proportions of sons and daughters was performed in a study investigating great tit hatchling sex ratios. We compare this test with a test based on an optimally weighted estimator recently proposed for medical studies with clustered binary data. According to our simulation results, this novel test is more powerful than the Wilcoxon signed rank test and should be used for the analysis of avian sex ratios. The methods are illustrated with real data from the great reed warbler.