Spreading of emergence over several years due to prolonged diapause in some larvae was shown in the chestnut weevil. Depending on the year the larvae buried themselves in the ground, 32-56% of live adults emerged after 2 or 3 years of underground life. Variability in the duration of diapause was assumed to correspond to tactics of adaptative "coin-flipping" plasticity. This plasticity must allow the chestnut weevil to respond to the unpredictability of its habitat as measured by the irregularity of chestnut production and summer drought. Indeed, fecundity and adult longevity did not lessen after 2 years of underground life. No drastic decrease in the population size of weevils occurs after bad years; for instance when the number of chestnuts on the study tree is less than 10 000, passers-by can collect all the fruit and about 95% of larvae developing in chestnuts are destroyed. Diapause nature (simple or prolonged) may be related to moisture and gas rates in the ground from October to December. These factors acting in autumn are not known to be involved in the physiological mechanisms that control the production of chestnuts.

We test the adaptive value of clutch size observed in a natural population of the chestnut weevil Curculio elephas. Clutch size is defined as the number of immatures per infested chestnut. In natural conditions, clutch size averages 1.7 eggs. By manipulating clutch size in the field, we demonstrate that deviations from the theoretical "Lack clutch size", estimated as eight immatures, are mainly due to proximate and delayed effects of clutch size on offspring performance. We show the existence of a trade-off between clutch size and larval weight. The latter, a key life-history trait, is highly correlated with fitness because it is a strong determinant of larval survival and potential fecundity of offspring females. The fitness of different potential oviposition strategies characterized by their clutch sizes, ranging from one to nine immatures, was calculated from field- estimated parameters. Chestnut weevil females obtain an evolutionary advantage by laying their eggs singly, since, for instance, fitness of single-egg clutches exceeds fitness of two-egg clutches and four-egg clutches by 8.0% and 15.1% respectively.

Bacterial processes in soil, including biodegradation, require contact between bacteria and substrates. Knowledge of the three-dimensional spatial distribution of bacteria at the microscale is necessary to understand and predict such processes. Using a soil microsampling strategy combined with a mathematical spatial analysis, we studied the spatial distribution of 2,4-dichlorophenoxyacetic acid (

Detection of interspecific competition between insects is often sensitive to scaling. We give an example of scale-dependent interference between the weevil Curculio elephas and the moth Cydia splendana, which both have larvae that develop in the fruits of chestnut Castanea sativa. Measures at three scales were considered: chestnut, husk (with one to three fertile fruits) and tree. Data come from observations in the field over 14 years, complemented by experiments done directly in trees. Data on individual chestnut fruits revealed a marked statistical interference between the two insects. Experiments demonstrated that presence of a moth larva in a fruit usually inhibits weevil egg-laying. Conversely, weevil presence does not strongly modify moth larval behavior. Cases of double infestation often correspond to fruits first attacked by the weevil. With measures on husks, interference between the two insects was observed only in some trees; its intensity was always weaker than in the chestnuts themselves. At the scale of entire trees, rates of infestation by each insect are not correlated. Interference in chestnut fruits is interpreted by assuming that the weevil female either is sensitive to a repellent molecule originating from a moth larva or its frass, or can detect moth larval sounds. Mechanisms governing infestation rates from data per tree are discussed in relation to those found at fruit scale and to plant-insect interactions. The need to estimate available resources both from quantitative and qualitative points of view is emphasized.

No abstract

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Ecological Society of America is collaborating with JSTOR to digitize, preserve and extend access to Ecology.Abstract. Many ecologists use two-dimensional systematic sampling to estimate mean density of individuals over the domain sampled. They usually calculate the variance of the mean as if the sample were a simple random sample, using the unbiased estimator under this sampling design, that is, &/n. This practice leads to a selection bias, i.e., incorrect inclusion probabilities of population units in the sample are used in the estimator of variance of the mean. The magnitude of the bias varies with the underlying spatial autocorrelation structure.Design-based inference and model-based inference are two conceptual frameworks for tackling estimation of variance of the mean in a systematic sample. This paper reports use of the geostatistical estimation variance a' with the model-based approach. This variance of the overall spatial mean depends on the spatial autocorrelation structure of the data. We illustrate the method by considering the density of acorns fallen under a sessile oak during one season. Acorns were numbered in square quadrats of 0.25 m2; the data set was exhaustive. We drew nine one-start systematic samples from the whole population of quadrats. We computed semivariograms for the whole population and each sample and fitted them to exponential models without nugget. Using geostatistical theoretical results, we calculated a variance of the mean density of acorns by Monte Carlo integration.We show that our variance estimate depends on (1) the origin of the systematic sample, the central sample being the most accurate, (2) quadrat size, with a decrease in the variance when quadrat size increases, (3) the semivariogram model, (4) discretization of the domain used in Monte Carlo integration, and (5) the random number generator. Considering all sources of variation, the variance estimate we calculated ranged from -2 to 36 in our example, for an overall mean equal to 95 acorns per quadrat. Geostatistical variance mainly reflects the locations and size of the sampled quadrats, and the spatial autocorrelation function.In our example, using the variance estimator of a simple random sample leads to a high selection bias. The bias can be neglected only in the absence of significant spatial autocorrelation in the sample. Otherwise, we advocate a geostatistical model-based approach that accounts for spatial autocorrelation.ogists use many designs to select the sample units but the two main approaches are simple random sampling (SRS) and systematic sampling (Southwood 1968, Chessel 1978. In an SRS, each unit is drawn randomly using a random number, but in single-start systematic ...

No abstract

Many ecologists use two‐dimensional systematic sampling to estimate mean density of individuals over the domain sampled. They usually calculate the variance of the mean as if the sample were a simple random sample, using the unbiased estimator under this sampling design, that is, σ2/n. This practice leads to a selection bias, i.e., incorrect inclusion probabilities of population units in the sample are used in the estimator of variance of the mean. The magnitude of the bias varies with the underlying spatial autocorrelation structure. Design‐based inference and model‐based inference are two conceptual frameworks for tackling estimation of variance of the mean in a systematic sample. This paper reports use of the geostatistical estimation variance σ2E with the model‐based approach. This variance of the overall spatial mean depends on the spatial autocorrelation structure of the data. We illustrate the method by considering the density of acorns fallen under a sessile oak during one season. Acorns were numbered in square quadrats of 0.25 m2; the data set was exhaustive. We drew nine one‐start systematic samples from the whole population of quadrats. We computed semivariograms for the whole population and each sample and fitted them to exponential models without nugget. Using geostatistical theoretical results, we calculated a variance of the mean density of acorns by Monte Carlo integration. We show that our variance estimate depends on (1) the origin of the systematic sample, the central sample being the most accurate, (2) quadrat size, with a decrease in the variance when quadrat size increases, (3) the semivariogram model, (4) discretization of the domain used in Monte Carlo integration, and (5) the random number generator. Considering all sources of variation, the variance estimate we calculated ranged from ∼2 to 36 in our example, for an overall mean equal to 95 acorns per quadrat. Geostatistical variance mainly reflects the locations and size of the sampled quadrats, and the spatial autocorrelation function. In our example, using the variance estimator of a simple random sample leads to a high selection bias. The bias can be neglected only in the absence of significant spatial autocorrelation in the sample. Otherwise, we advocate a geostatistical model‐based approach that accounts for spatial autocorrelation.