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A goodness-of-fit test for one-parameter count distributions with finite second moment is proposed. The test statistic is derived from the $L_1$-distance of a function of the probability generating function of the model under the null hypothesis and that of the random variable actually generating data, when the latter belongs to a suitable wide class of alternatives. The test statistic has a rather simple form and it is asymptotically normally distributed under the null hypothesis, allowing a straightforward implementation of the test. Moreover, the test is consistent for alternative distributions belonging to the class, but also for all the alternative distributions whose probability of zero is different from that under the null hypothesis. Thus, the use of the test is proposed and investigated also for alternatives not in the class. The finite-sample properties of the test are assessed by means of an extensive simulation study.

Nearest neighbor spatial interpolation for mapping continuous populations and finite populations of areas or units is approached from a design-based perspective, that is, populations are fixed, and uncertainty stems from the sampling scheme adopted to select locations. We derive conditions for design-based pointwise and uniform consistency of the nearest neighbor interpolators. We prove that consistency holds under certain schemes that are widely applied in environmental and forest surveys. Furthermore, we propose a pseudopopulation bootstrap estimator of the root mean squared errors of the interpolated values. Finally, a simulation study is performed to assess the theoretical results.

The estimation of a surface throughout a continuum of points in a study area is addressed by means of two-phase sampling strategies. To this aim, a family of two-phase inverse distance weighting interpolators is introduced, and their design-based asymptotic properties are derived when the surface remains fixed and the number of sample points approaches infinity. In particular, conditions ensuring asymptotic unbiasedness and consistency are derived and are proven to hold for some of the most widely applied environmental sampling schemes. Furthermore, a computationally simple mean squared error estimator is proposed. Finally, a simulation study is performed to assess the theoretical results. The proposed strategy is adopted to provide the map of basal area in a forested region of Casentino Valley (Central Italy).

The estimation of the values of a variable at any point of a study area is performed using Bernstein polynomials when the sampling scheme is implemented by selecting a point in each polygon of a regular grid overimposed onto the area. The evaluation of the precision of the resulting estimates is investigated under a completely design-based framework. Moreover, as the main contribution to the mean squared error of the Bernstein-type estimator is due to the bias, also a pseudo-jackknife estimator is proposed. The performance of both estimators is investigated theoretically and by means of a simulation study. An application to a soil survey performed in Berkshire Downs in Oxfordshire (UK) is considered.

SUMMARYWe analyse the estimation of values of a survey variable throughout a continuum of points in a study area when a sample of points is selected by a probabilistic sampling scheme. At each point, the value is estimated using an inverse distance weighting interpolator, and maps of the survey variable can be obtained. We investigate the design-based asymptotic properties of the interpolator when the study area remains fixed and the number of sampled points approaches infinity, and we derive conditions ensuring design-based asymptotic unbiasedness and consistency. The conditions essentially require the existence of a pointwise or uniformly continuous function describing the behaviour of the survey variable and the use of spatially balanced designs to select points. Finally, we propose a computationally simple mean squared error estimator.

Spatial populations are usually located on a continuous support. They can be surfaces representing the values of the survey variable at any location, finite collections of units with the corresponding values of the survey variable, or finite collections of areal units partitioning the support, where the survey variable is the total amount of an attribute within. We derive conditions on the design sequence ensuring consistency of the Horvitz–Thompson estimator of spatial population totals, supposing minimal requirements on the survey variable. A simulation study is performed to check theoretical results. Consistency and its implications in real surveys are discussed with focus on environmental surveys.

Several studies reveal that there is a strong interconnection between climate change and biodiversity. Indeed, estimating plant biodiversity is an important issue under forest ecosystem monitoring, which allows the evaluation of carbon storage and sequestration capacity. To this end, a two‐phase strategy, suitably compatible with the most adopted sampling designs in large‐scale forest inventories, is proposed. In the first phase, tessellation stratified sampling is performed by partitioning the study area into a grid of quadrats and by randomly selecting a point in each quadrat. The first‐phase points are classified as forest or nonforest using remotely sensed imagery. In the second phase, a sample of points is selected from those classified as forest by means of simple random sampling without replacement. The second‐phase points constitute the centers of circular plots that are visited in the field to record plant species (usually trees) and their abundance. Estimators of abundance and diversity and estimators of their variances are presented. The proposed strategy is applied in a forest area from Central Italy, as a case study. With respect to the sampling effort, the resulting estimates of relative standard errors are satisfactory, especially those regarding the overall total and diversity index estimators. The proposed statistical approach represents a suitable reference for integrated forest inventory frameworks effectively supporting biodiversity monitoring and assessment.

Forest attributes such as volume or basal area are concentrated at tree locations and are absent elsewhere. It is, therefore, more meaningful to consider the amount of forest attributes at a prefixed spatial grain, within regular plots of prefixed size centered at the points of the study area. In this way, the diversity of attributes within plots also can be considered and quantified by suitable indexes, giving rise to a diversity surface defined on the continuum of points constituting the area. We analyze the estimation of diversity surfaces when a sample of plots is selected by a probabilistic sampling scheme and diversity within nonsampled plots is estimated using an inverse distance weighting interpolator. We discuss the design-based asymptotic properties of the resulting maps when the survey area remains fixed and the number of sampled points increases. Because diversity surfaces share suitable mathematical properties, if the schemes adopted to select sample points ensure an even coverage of the study areas avoiding large portions of non-sampled zones, it can be proven that the estimated maps approach the true maps.

The study of animal diet and feeding behaviour is a fundamental tool for the illustration of the ecological role of species in the ecosystem. However, size and quality of food intake samples make it hard for researchers to describe the diet composition of many small species. In our study, we exploited genomic tools for the analysis of the diet composition of the Savi’s pine vole (Microtus savii) using DNA barcoding and qPCR techniques for the identification of ingested plant species retrieved from stomach contents. In contrast with previous studies, we found that, despite being a fossorial species, the Savi’s pine vole is a selective feeder that undergoes intense superficial activity in search for food. In addition, our study shows that with a a priori knowledge of the candidate plant species included in animal diet, qPCR is a powerful tool to assess presence/absence, frequency of occurrence and electivity of ingested species. We conclude that this approach offers new opportunities to implement the analysis of food selection in small animals, thereby revealing a detailed picture of plant-animal interactions.

The lists of species obtained by purposive sampling by field ecologists can be used to improve the sample-based estimation of species richness. A new estimator is here proposed as a modification of the difference estimator in which the species inclusion probabilities are estimated by means of the species frequencies from incidence data. If the species list used to support the estimation is complete the estimator guesses the true richness without error. In the case of incomplete lists, the estimator provides values invariably greater than the number of species detected by the combination of sample-based and purposive surveys. An asymptotically conservative estimator of the mean squared error is also provided. A simulation study based on two artificial communities is carried out in order to check the obvious increase in accuracy and precision with respect to the widely applied estimators based on the sole sample information. Finally, the proposed estimator is adopted to estimate species richness in the Maremma Regional Park, Italy.

International audience; AbstractKey messageTrees outside forests (TOF) have crucial ecological and social-economic roles in rural and urban contexts around the world. We demonstrate that a large-scale estimation strategy, based on a two-phase inventory approach, effectively supports the assessment of TOF’s diversity and related climate change mitigation potential.ContextAlthough trees outside forest (TOF) affect the ecological quality and contribute to increase the social and economic developments at various scales, lack of data and difficulties to harmonize the known information currently limit their integration into national and global forest inventories.AimsThis study aims to develop and test a large-scale estimation framework to assess ecological diversity and above-ground carbon stock of TOF.MethodsThis study adopts a two-phase inventory approach.ResultsIn the surveyed territory (Molise region, Central Italy), all the attributes considered (tree abundance, basal area, wood volume, above-ground carbon stock) are concentrated in a few dominant species. Furthermore, carbon stock in TOF above-ground biomass is non-negligible (on average: 28.6 t ha−1). Compared with the low field sampling effort (0.08% out of 52,796 TOF elements), resulting uncertainty of the estimators are more than satisfactory, especially those regarding the diversity index estimators (relative standard errors

The estimation of diversity indexes is considered when species abundance is estimated by means of plots, points or transects placed onto the study region in accordance with probabilistic schemes. Under uniform random sampling–that is when plots, points or transects are uniformly and independently located on the study region–the consistency and the large-sample normality of diversity index estimators follow from the usual limit theorems as the sampling effort increases. In addition, variance estimation and finite-sample bias reduction are achieved by means of standard results on the jackknife method. Despite its theoretical simplicity, uniform random sampling may lead to uneven coverage of the study region. In order to avoid this shortcoming, the use of one-per-stratum stratified sampling is usually considered. This sampling scheme involves partitioning the study region into subsets of equal size and uniformly selecting a plot, a point or a transect in each of these subsets. The present paper aims to show the consistency and the large-sample normality of the diversity index estimators under one-per-stratum stratified sampling, as well as to deal with variance estimation and bias reduction. Copyright © 2015 John Wiley & Sons, Ltd.

The crested porcupine Hystrix cristata is a large rodent which pairs for life. We studied the space use of 17 female and 9 male radio-tracked porcupines in an evergreen coastal woodland (“macchia”, EW) and in an agricultural estate (AE), with special reference to the use of cultivations. Home range sizes of male porcupines (4.72–323.40) ranged around 114 ha (median) during the warm period (April–September) and 162 ha during the cold one (October–March). Home ranges of females (2.48–323.40) were c. 91 ha during the warm period and c. 143 ha during the cold one. Habitat composition and selection changed from the cold to the warm months, with porcupines being present in agricultural areas especially in the latter. Home range overlap between members of the same pair varied from 57% to 97% (median, 75%). Habitat selection was analyzed at the second (within study area) and at the third (within home range) order of selection. Within study areas, porcupines avoided cultivations and selected habitats with dense vegetation, providing cover and food. Within home ranges, in the warm period, porcupines selected agricultural areas in EW, where this habitat represented a minor portion of the study site. In that season, the Mediterranean “macchia” is a poor source of food, forcing porcupines to travel long distances to reach feeding sites. No significant difference of habitat selection within home ranges was detected between members of the same pair. Cultivations may play a key-role for porcupine survival, especially in poor habitats, as they provide abundant food resources in the warm period.

We used distance sampling to assess density and the detectability of male black grouse (Tetrao tetrix) and rock ptarmigan (Lagopus muta) in 2 protected areas of the Italian Alps. Our sampling effort was not sufficient to provide reliable inference for monitoring projects. Therefore, we used our field results to structure a simulation study to compare the performance of plot-based and distance sampling estimators of density. These 2 methods have different assumptions: plot sampling assumes a perfect detection of animals within the surveyed plots, whereas distance sampling assumes a decrease in detectability as the distance between observer and animal increases. The density and the detectability conditions adopted in the simulation were designed to be similar to those observed in our 2 studies, whereas the spatial patterns presumed for the simulated populations described a wide range of possible scenarios. Sampling points were allocated according to both random and stratified distributions. Simulation results showed that plot sampling underestimated density with bias invariably greater than 30% and confidence intervals with coverage lower than the nominal level of 95%. Conversely, distance sampling estimators provided bias levels invariably smaller than those obtained using plot sampling and bootstrap confidence intervals with empirical coverage near to or greater than 95%. Based on our simulations, the distance sampling estimator was superior to the plot sampling estimator for these grouse species on our study areas in terms of precision and accuracy and the stratified allocation was superior to the random allocation. However, distance sampling would be very costly to implement. Based on our simulations, 4–5 points per km2 would be necessary to achieve reliable estimates of density and density changes. If distance sampling cannot be completed at this intensity, other sampling methods should be adopted. © 2014 The Wildlife Society.

The analysis of habitat selection in radio-tagged animals is approached by comparing the portions of use against the portions of availability observed for each habitat type. Since data are linearly dependent with singular variance-covariance matrices, standard multivariate statistical tests cannot be applied. To bypass the problem, compositional data analysis is customarily performed via log-ratio transform of sample observations. The procedure is criticized in this paper, emphasizing the several drawbacks which may arise from the use of compositional analysis. An alternative nonparametric solution is proposed in the framework of multiple testing. The habitat use is assessed separately for each habitat type by means of the sign test performed on the original observations. The resulting p values are combined in an overall test statistic whose significance is determined permuting sample observations. The theoretical findings of the paper are checked by simulation studies. Applications to case studies previously considered in literature are discussed.

To bypass the methodological problems involved in the use of compositional data analysis by Aebischer, Robertson and Kenward (1993), the assessment of proportional habitat use is approached in a multiple-testing framework exploiting data from radio-tagged animals. The habitat use is assessed separately for each habitat type by means of the sign test to compare the proportion of use vs the proportion of availability. The resulting p-values are combined in an overall test statistic whose significance is determined permuting sample observations. The “phuassess” package for the R software, available from Comprehensive R Archive Network (CRAN), allows to straightforwardly perform the assessment. The package is presented and described and, in order to exemplify its application, habitat selection is assessed in a population of European brown hares (Lepus europaeus) settled in central Italy.

A number of international agreements and commitments emphasize the importance of appropriate monitoring protocols and assessments as prerequisites for sound conservation and management of the world’s forest ecosystems. Mandated periodic surveys, like forest inventories, provide a unique opportunity to identify and properly satisfy natural resource management information needs. Distinctively, there is an increasing need for detecting diversity by means of unambiguous diversity measures. Because all diversity measures are functions of tree species abundances, estimation of tree diversity indices and profiles is inevitably performed by estimating tree species abundances and then estimating indices and profiles as functions of the abundance estimates. This strategy can be readily implemented in the framework of current forest inventory approaches, where tree species abundances are routinely estimated by means of plots placed onto the surveyed area in accordance with probabilistic schemes. The purpose of this paper is to assess the effectiveness of this strategy by reviewing theoretical results from published case studies. Under uniform random sampling (URS), that is when plots are uniformly and independently located on the study region, consistency and asymptotic normality of diversity index estimators follow from standard limit theorems as the sampling effort increases. In addition, variance estimation and bias reduction are achieved using the jackknife method. Despite its theoretical simplicity, URS may lead to uneven coverage of the study region. In order to avoid unbalanced sampling, the use of tessellation stratified sampling (TSS) is suggested. TSS involves covering the study region by a polygonal grid and randomly selecting a plot in each polygon. Under TSS, the diversity index estimators are consistent, asymptotically normal and more precise than those achieved using URS. Variance estimation is possible and there is no need to reduce bias.

The estimation of forest carbon sources and sinks is an important issue in forest inventories. For monitoring forest carbon budgets, a two-phase strategy is proposed. In the first phase, N points are thrown onto the study region by means of unaligned systematic sampling and then classified as forest or non-forest on the basis of aerial information. In the second phase, a sample of n points of those classified as forest is achieved by means of a sampling scheme with selection probabilities proportional to the sizes of an auxiliary variable. Subsequently, circular plots of adequate size are centred in the n selected points and visited in order to quantify the carbon amount within. Unbiased estimators of the forest area size as well as of the overall amount of carbon in that area and a conservative estimator of their variance–covariance matrix are derived. The strategy is applied to estimate carbon budgets in the forests of Trentino (North Italy).

The monitoring of population trends of wild ungulates is important to evaluate their population dynamics and to develop sound conservation/management plans. The wild boar Sus scrofa can impose heavy impacts on ecosytems and human activities, as well as be responsible for disease transmission. Estimating abundance of wild boars is a challenging issue, because of some peculiar biological and ecological traits of this ungulate. Indices of relative abundance could be used to evaluate its population trends. In a Mediterranean area we used faeces counts, through a two-stage stratified sampling, to estimate relative densities of wild boars, between 2007 and 2014. Faeces density estimates increased not significantly between 2007 (151.5 faeces/100 ha) and 2010 (203.8 faeces/100 ha) and decreased significantly from 2010 to 2014 (95.5 faeces/100 ha). The decrease in faeces density estimates was consistent with the increased harvest effort (number of harvest days), performed from 2010 to 2013 to limit impact on ecosystems and reduce damages to crops. The variation of faeces density estimates was also consistent with that of harvest indices (total harvest to harvest effort), with significantly positive values of Pearson and rank correlation coefficients. Results suggest that faeces density estimates achieved with the adopted sampling strategies can be effectively used as indices of relative abundance.

This article considers a two-phase estimation for the areal extent of K land categories partitioning a study region and a three-phase estimation for the biomass of W forest categories out of the K. In the first phase, a sample of N points is selected according to the unaligned systematic sampling. In the second phase, the selected points are partitioned into L strata on the basis of aerial photos. Then, a total sample of n < N points is selected by stratified sampling and the selected points are visited on the ground and correctly classified into one of K categories. The information achieved in the second phase is sufficient for obtaining an unbiased estimator of the areal extent vector together with a conservative estimator of its variance-covariance matrix. As to the estimation of the biomass of the W forest categories, in the third phase the second-phase sample is further partitioned into substrata on the basis of ground information. Finally, a total sample of m < n points is selected by stratified sampling. Then a plot of adequate radius centered at each point is considered and the biomass is recorded within. An unbiased estimator of the biomass vector is derived together with a conservative estimator of its variance-covariance matrix. The proposed strategy also makes it possible to obtain the calibrated estimator of the areal extent vector as well as estimators for the sums or ratios of the areal extents and biomasses. The application of the strategy in the Italian National Forest Inventory is considered.

In replicated sampling protocols, each design replicate is independently and randomly placed onto the study region. Subsequently, in order to estimate the objective parameter, the Horvitz–Thompson (HT) estimator is usually considered for each design replicate and an overall estimator results from the average of the single estimators. Obviously, the procedure gives rise to a sample of HT estimators under simple random sampling (SRS) in such a way that the objective parameter is estimated by the corresponding sample mean. However, this procedure is likely to produce uneven coverage of the region and hence a large variability of the overall estimator. Therefore, to avoid such drawbacks, a quasi-systematic protocol for the design replicates is proposed. In this case, the suggested procedure gives rise to a sample of HT estimators under steady-state ranked set sampling (SRSS)–a generalization of well-known ranked set sampling (RSS)–so as to estimate the objective parameter by the corresponding sample mean. The proposed method produces large efficiency gains and does not involve supplementary sampling costs or extra field work. Copyright © 2003 John Wiley & Sons, Ltd.

An original cross sectional dataset referring to a medium sized Italian university is implemented in order to analyze the determinants of scientific research production at individual level. The dataset includes 942 permanent researchers of various scientific sectors for a three year time span (2008 - 2010). Three different indicators - based on the number of publications or citations - are considered as response variables. The corresponding distributions are highly skewed and display an excess of zero - valued observations. In this setting, the goodness of fit of several Poisson mixture regression models are explored by assuming an extensive set of explanatory variables. As to the personal observable characteristics of the researchers, the results emphasize the age effect and the gender productivity gap, as previously documented by existing studies. Analogously, the analysis confirm that productivity is strongly affected by the publication and citation practices adopted in different scientific disciplines. The empirical evidence on the connection between teaching and research activities suggests that no univocal substitution or complementarity thesis can be claimed: a major teaching load does not affect the odds to be a non-active researcher and does not significantly reduce the number of publications for active researchers. In addition, new evidence emerges on the effect of researchers administrative tasks, which seem to be negatively related with researcher's productivity, and on the composition of departments. Researchers' productivity is apparently enhanced by operating in department filled with more administrative and technical staff, and it is not significantly affected by the composition of the department in terms of senior or junior researchers., Comment: Revised version accepted for publication by Scientometrics

The ecological and economical relevance of sweet chestnut (Castanea sativa Mill.) has long been related to its widespread geographical distribution and multipurpose product potential. In Italy, chestnut management represents a paradigmatic example of the potential conflict between landowner targets and tree species diversity conservation. Distinctively, the relationships between silvicultural treatment and tree species diversity of chestnut coppices are here investigated by means of diversity profiles to assess tree diversity of six stands in Central Italy. The stands were purposively selected in such a way to be characterized by the same site conditions but with different silvicultural features (age, number of thinning). Plot sampling was performed across the stands and their tree diversity was compared and ordered by means of intrinsic diversity profiles estimated from the sample data. The achieved results suggest alternative suitable options for managing chestnut coppice stands in order to enhance tree biodiversity while maintaining timber production.

SUMMARY We analyse design-based properties of two-phase strategies for estimating totals and nonlinear functions of totals for environmental populations when the sampling schemes are uniquely determined by points placed in the study region. In the first phase, points are located using tessellation stratified sampling, whereas in the second phase a finite population sampling scheme is adopted. We give sufficient conditions on second-phase designs that ensure consistency, and we investigate the variance convergence rate for some familiar schemes.

A design-based strategy for estimating wildlife ungulate abundance in a Mediterranean protected area (Maremma Regional Park) is considered. The estimation is based on pellet group count (clearance count technique) in a set of plots, whose size and number is established on the basis of practical considerations and available resources. The sampling scheme involves a preliminary stratification and subsequent two-stage sampling. In the first stage, large strata (defined through habitat features) are partitioned into spatial units and a sample of units is selected by means of a sampling scheme ensuring inclusion probabilities proportional to unit size, but avoiding the selection of contiguous units. Then, the abundances of the selected units are estimated in a second stage, in which plots are located using a random scheme ensuring an even coverage of the units. In small strata, only the second stage is performed. Unbiased estimators of abundance and conservative estimators of their variances are derived for each strata and for the whole study area. The proposed strategy has been applied since the Summer of 2006 and the estimation results reveal substantial improvement with respect to the previous results obtained by means of an alternative strategy.

Summary 1 For many species and circumstances, mark–resighting procedures constitute valid alternatives to capture–recapture methods. Indeed, resightings are generally cheaper to acquire than physically recapturing and rehandling the animals, especially when radiotelemetry or other tracking devices are available. 2 In order to estimate population abundance, the joint hypergeometric maximum likelihood estimator, the Minta–Mangel estimator and the Bowden estimator are implemented in noremark, software which has become very popular with biologists in the past decade. 3 In this paper, the basic assumptions regarding these widely applied procedures are delineated and discussed. A simulation study is performed in order to investigate the robustness of the estimators under failure of the assumptions. 4 Theoretical considerations and simulation results motivate the use of the Bowden estimator which, when marks are distributed quite evenly among groups, constitutes the sole reliable method, offering computational simplicity and robustness. On the other hand, if the marks are distributed unevenly, no mark–resighting procedure seems reliable. An application to a case study is considered.

Sample surveys for estimating the abundance of wildlife ungulate populations are considered in a design-based approach. On the basis of previous theoretical results, a two-stage sampling is proposed. In the first stage, some spatial units are selected using Lahiri-Midzuno sampling, while in the second stage, the animal abundance in the selected units is estimated by means of plot sampling performed on the faecal accumulation within the units. The statistical properties of the resulting ratio estimator of abundance are outlined. An application of the proposed method for estimating fallow-deer and roe-deer abundance in Maremma Regional Park is described.

A two-phase estimation of coverages of the k land categories partitioning a study area is considered. In the first phase a sample of N points is selected according to a spatial sampling design and the selected points are assigned to the k categories on the basis of satellite imagery or aerial photos. Simple random sampling, stratified sampling and systematic sampling of points are considered. The theoretical results suggest the use of stratified sampling as the best design-based spatial strategy in the first phase. Subsequently, in the second phase, a sample of n

The statistical properties of two-stage plot sampling estimators of abundance are considered. In the first stage, some spatial units are selected over the whole study area according to a suitable sampling design, while in the second stage, the selected units are surveyed with floating plot sampling to estimate the abundance within. Some insights into the accuracy of the resulting estimators are obtained by splitting the sample variance into the first and second-stage components, while performance is empirically checked by means of a simulation study. Simulation results show that, in most situations, a relevant amount of the overall variance is due to the second stage sampling.

In practical ecological sampling studies, a certain design (such as plot sampling or line-intercept sampling) is usually replicated more than once. For each replication, the Horvitz-Thompson estimation of the objective parameter is considered. Finally, an overall estimator is achieved by averaging the single Horvitz-Thompson estimators. Because the design replications are drawn independently and under the same conditions, the overall estimator is simply the sample mean of the Horvitz-Thompson estimators under simple random sampling. This procedure may be wisely improved by using ranked set sampling. Hence, we propose the replicated protocol under ranked set sampling, which gives rise to a more accurate estimation than the replicated protocol under simple random sampling.

A two-stage estimation of biological population abundance is considered at large scale. In the first stage some area units are selected using without-replacement sampling, while in the second stage the estimation of abundance in each selected unit is performed through a suitable counting strategy. In order to account for the variability due to varying sizes, the use of unit size as an auxiliary variable is proposed at the design level by handling the inclusion probabilities of the units, at the estimation level by combining the single abundance estimates as in the ratio and regression criteria, or at both levels. The results of an artificial comparison suggest the joint use of simple random sampling and ratio criterion. The asymptotic properties of the resulting estimator are derived when the estimation of abundances at the second stage is performed through the replicated use of the counting strategy.