Distributions et structures spatiales au sein des patchs hiérarchiques dynamiques : mise en évidence des relations spatiales espèce-environnement à échelles multiples

The identification of relevant spatial scales in organism-environment relationships is a key step in the understanding of an ecosystem. To study this concern, the hierarchical patch dynamic theory offers a unique concept to relate the ecological processes, patterns and scales. Based on this theory, we approach the identification of relevant spatial scale in organism-environment relationships, with the intention to explicitly account for the spatial component of ecological processes. From the comparison between the PCNM method and the geostatistical approach applied to spruce's defoliation caused by spruce budworm in Ontario (Canada), the geostatistical approach is more efficient to identify relevant spatial scale inorganism-environment relationships. Even so, to answer the question of significance of identified scales, we developed a Monte-Carlo test for nested variogram models. However classical geostatistics could be difficult to use with ecological data which have positive, skewed distribution. To consider this problem, we developed a hierarchical model associated to geostatistics and applied it to modelling spatial distribution of auks (Uria spp) in the Bay of Biscay. Tree levels of patches were characterized: a very broad scale patch (200 km), broad scale patches (50 km) and fine scale patches (10 km). The spatio-temporal analysis of links between auk distribution and the oceanographic landscape discriminates between the "process variables" which feature a stable link in term of sign and correlation among time and "circumstance variables" which have unstable link. At very broad scale, the surface salinity, the mixed layer depth and the chlorophyll a, could be view as "process variables" which have been used to define the potential habitat of auks during the wintering season. At broad scale, only chlorophyll a is selected as a process variable, giving a path to model preferential habitats. These two kinds of habitats can then be connected to different levels of a hierarchical patch dynamic system.L'identification des échelles spatiales pertinentes dans l'étude des interactions espèces-environnement est indispensable pour avoir une meilleure compréhension du fonctionnement des écosystèmes. Pour aborder cette problématique la théorie des patchs hiérarchiques offre un principe unificateur permettant de relier les concepts de processus écologiques, de pattern et d'échelles. La mise en évidence des différentes échelles destructuration des organismes et des liens avec leur environnement a été abordée en se basant sur cette théorie et avec la volonté de prendre en compte de manière explicite la composante spatiale du processus étudié. A partir de la comparaison entre la PCNM et les géostatistiques, appliquées aux défoliations causées par une chenille tordeuse de bourgeons sur les épineux de l'Ontario (Canada), les géostatistiques sont apparues plus efficaces dans l'identification des relations espèces-environnement à différentes échelles. Restait la question de la significativité des échelles identifiées, pour cela un test par simulation Monte-Carlo a été développé pour les modèles de variogrammes emboîtés. Cependant, les géostatistiques classiques peuvent être difficile à utiliser avec des données écologiques présentant des distributions discrètes et fortement dissymétriques. Pour palier ces difficultés, un modèle hiérarchique couplé aux géostatistiques a été développé et appliqué à la modélisation de la distribution des guillemots (Uria spp) dans le golfe de Gascogne. Trois niveaux de structuration spatiale ont été caractérisés: un pattern à trèslarge échelle (200 km), un pattern à moyenne échelle (50 km) et un pattern à fine échelle (10 km). L'analyse spatio-temporelle des liens nous a permis de distinguer deux types de variables : les variables de processus pour lesquelles le lien est stable en terme de corrélation et de signe au cours du temps ; et les variables de circonstances pour lesquelles le lien n'est pas stable. A très large échelle, nous avons identifié trois variables de processus : la salinité de surface, la profondeur de la couche de mélange et la chlorophylle a, qui nous permettent de modéliser l'habitatpotentiel des guillemots. A moyenne échelle, seule la chlorophylle a est reconnue comme une variable de processus, donnant une piste pour modéliser les habitats préférentiels. Ces deux types d'habitats peuvent être reliés aux niveaux d'un système en patchs hiérarchiques dynamique

Mixed interactions among life history stages of two harvested related species

Climate change and harvesting can affect the ecosystems' functioning by altering the population dynamics and interactions among species. Knowing how species interact is essential for better understanding potentially unintended consequences of harvest on multiple species in ecosystems. I analyzed how stage-specific interactions between two harvested competitors, the haddock (Melanogrammus aeglefinus) and Atlantic cod (Gadus morhua), living in the Barents Sea affect the outcome of changes in the harvest of the two species. Using state-space models that account for observation errors and stochasticity in the population dynamics, I run different harvesting scenarios and track population-level responses of both species. The increasing temperature elevated the number of larvae of haddock but did not significantly influence the older age-classes. The nature of the interactions between both species shifted from predator-prey to competition around age-2 to -3. Increased cod fishing mortality, which led to decreasing abundance of cod, was associated with an increasing overall abundance of haddock, which suggests compensatory dynamics of both species. From a stage-specific approach, I show that a change in the abundance in one species may propagate to other species, threatening the exploited species' recovery. Thus, this study demonstrates that considering interactions among life history stages of harvested species is essential to enhance species' co-existence in harvested ecosystems. The approach developed in this study steps forward the analyses of effects of harvest and climate in multi-species systems by considering the comprehension of complex ecological processes to facilitate the sustainable use of natural resources

Distributions et structures spatiales au sein de systèmes en patchs hiérarchiques dynamiques : mise en évidence des relations spatiales espèce-environnement à échelles multiples

10 annexes 106 p. Diplôme : Dr. d'UniversiteThe identification of relevant spatial scales in organism-environment relationships is a key step in the understanding of an ecosystem. To study this concern, the hierarchical patch dynamic theory offers a unique concept to relate the ecological processes, patterns and scales. Based on this theory, we approach the identification of relevant spatial scale in organism-environment relationships, with the intention to explicitly account for the spatial component of ecological processes. From the comparison between the PCNM method and the geostatistical approach applied to spruce’s defoliation caused by spruce budworm in Ontario (Canada), the geostatistical approach is more efficient to identify relevant spatial scale in organism-environment relationships. Even so, to answer the question of significance of identified scales, we developed a Monte-Carlo test for nested variogram models. However classical geostatistics could be difficult to use with ecological data which have positive, skewed distribution. To consider this problem, we developed a hierarchical model associated to geostatistics and applied it to modelling spatial distribution of auks (Uria spp) in the Bay of Biscay. Tree levels of patches were characterized : a very broad scale patch (200 km), broad scale patches (50 km) and fine scale patches (10 km). The spatio-temporal analysis of links between auk distribution and the oceanographic landscape discriminates between the ”process variables” which feature a stable link in term of sign and correlation among time and ”circumstance variables” which have unstable link. At very broad scale, the surface salinity, the mixed layer depth and the chlorophyll a, could be view as ”process variables” which have been used to define the potential habitat of auks during the wintering season. At broad scale, only chlorophyll a is selected as a process variable, giving a path to model preferential habitats. These two kinds of habitats can then be connected to different levels of a hierarchical patch dynamic syste

Distributions et structures spatiales au sein de systèmes en patchs hiérarchiques dynamiques (mise en évidence des relations spatiales espèce-environnement à échelles multiples)

L identification des echelles spatiales pertinentes dans l etude des interactions espèces-environnement est indispensable pour avoir une meilleure compréhension du fonctionnement des ecosystèmes. Pour aborder cette probl ematique la théorie des patchs hiérarchiques offre un principe unificateur permettant de relier les concepts de processus ecologiques, de pattern et d echelles. La mise en evidence des différentes echelles de structuration des organismes et des liens avec leur environnement a eté abordée en se basant sur cette théorie et avec la volonté de prendre en compte de manière explicite la composante spatiale du processus etudié. A partir de la comparaison entre la PCNM et les géostatistiques, appliquées aux d efoliations caus ees par une chenille tordeuse de bourgeons sur les epineux de l Ontario (Canada), les g eostatistiques sont apparues plus efficaces dans l identification des relations especes-environnement a différentes echelles. Restait la question de la significativite des echelles identifiées, pour cela un test par simulation Monte-Carlo a eté d eveloppé pour les modèles de variogrammes emboıtés. Cependant, les géostatistiques classiques peuvent etre difficile a utiliser avec des donn ees ecologiques pr esentant des distributions discrètes et fortement dissymétriques.Pour palier ces difficultés, un modèle hiérarchique couplé aux géostatistiques a eté développé et appliqué a la modélisation de la distribution des guillemots (Uria spp) dans le golfe de Gascogne. Trois niveaux de structuration spatiale ont etécaractérisés : un pattern à très large echelle (200 km), un pattern a moyenne echelle (50 km) et un pattern a fine echelle (10 km). L analyse spatio-temporelle des liens nous a permis de distinguer deux types de variables : les variables de processus pour lesquelles le lien est stable en terme de corr elation et de signe au cours du temps ; et les variables de circonstances pour lesquelles le lien n est pas stable. A très large échelle, nous avons identifié trois variables de processus : la salinité de surface, la profondeur de la couche de mélange et la chlorophylle a, qui nous permettent de modéliser l habitat potentiel des guillemots. A moyenne echelle, seule la chlorophylle est reconnue comme une variable de processus, donnant une piste pour modéliser les habitats préférentiels. Ces deux types d habitats peuvent etre reliés aux niveaux d un système en patchs hiérarchiques dynamique.The identification of relevant spatial scales in organism-environment relationships is a key step in the understanding of an ecosystem. To study this concern, the hierarchical patch dynamic theory offers a unique concept to relate the ecological processes, patterns and scales. Based on this theory, we approach the identification of relevant spatial scale in organism-environment relationships, with the intention to explicitly account for the spatial component of ecological processes. From the comparison between the PCNM method and the geostatistical approach applied to spruce s defoliation caused by spruce budworm in Ontario (Canada), the geostatistical approach is more efficient to identify relevant spatial scale in organism-environment relationships. Even so, to answer the question of significance of identified scales, we developed a Monte-Carlo test for nested variogram models. However classical geostatistics could be difficult to use with ecological data which have positive, skewed distribution. To consider this problem, we developed a hierarchical model associated to geostatistics and applied it to modelling spatial distribution of auks (Uria spp) in the Bay of Biscay. Tree levels of patches were characterized : a very broad scale patch (200 km), broad scale patches (50 km) and fine scale patches (10 km). The spatio-temporal analysis of links between auk distribution and the oceanographic landscape discriminates between the process variables which feature a stable link in term of sign and correlation among time and circumstance variables which have unstable link. At very broad scale, the surface salinity, the mixed layer depth and the chlorophyll a, could be view as process variables which have been used to define the potential habitat of auks during the wintering season. At broad scale, only chlorophyll a is selected as a process variable, giving a path to model preferential habitats. These two kinds of habitats can then be connected to different levels of a hierarchical patch dynamic system.AIX-MARSEILLE2-BU Sci.Luminy (130552106) / SudocSudocFranceF

A spatial covariance model with a single wave effect and a finite range

International audienceWe propose a new spatial covariance model with a single wave effect and a finite range. The construction of this model is based on a radial kernel composed of two concentric spheres and the finite range enables us to obtain an exact simulation from the circulant embedding method. This model responds to the lack of efficient covariance functions to model patterns characterized by concentration areas surrounded by gaps. We compare the single wave model to other existing hole effect models such as the Bessel model, the damped cosine model and the cardinal sine mode

A spatial covariance model with a single wave effect and a finite range

We propose a new spatial covariance model with a single wave effect and a finite range. The construction of this model is based on a radial kernel composed of two concentric spheres and the finite range enables us to obtain an exact simulation from the circulant embedding method. This model responds to the lack of efficient covariance functions to model patterns characterized by concentration areas surrounded by gaps. We compare the single wave model to other existing hole effect models such as the Bessel model, the damped cosine model and the cardinal sine model.

Data from: Relationships between vital rates and ecological traits in an avian community

1. Comparative studies about the relationships between vital rates and ecological traits at the community level are conspicuously lacking for most taxa because estimating vital rates requires detailed demographic data. Identifying relationships between vital rates and ecological traits could help to better understand ecological and evolutionary demographic mechanisms that lead to interspecific differences in vital rates. 2. We use novel dynamic N-mixture models for counts to achieve this for a whole avian community comprising 53 passerine species, while simultaneously accounting for density dependence and environmental stochasticity in recruitment and survival and, importantly, correcting our inferences for imperfect detection. Demographic stochasticity is taken into account in the form of the binomial and Poisson distributions describing survival events and number of recruits. We then explore relationships between estimated demographic parameters (i.e., vital rates) and ecological traits related to migration patterns, diet, habitat and nesting location of each species. 3. The relative importance of recruitment and adult survival as contributors to population growth varied greatly among species, and interspecific differences in vital rates partly reflected differences in ecological traits. Migratory mode was associated with interspecific differences in population growth and density dependence. Resident species had higher population growth rates than long- and short-distance migrants. We found no relationships between diet and population growth rate. Habitat differences were associated with different growth rates: alpine, wetland, and farmland species had lower population growth rates than forest species. Differences in population growth rates among nesting locations showed that breeding habitat is essential for population dynamics. 4. Our study reveals relationships between ecological traits and contributions of vital rates to population growth and suggests ways in which patterns of population growth fluctuations in a community might be determined by life history

Geostatistical modelling of wildlife populations: a non-stationary hierarchical model for count data

International audienceWe propose a hierarchical model coupled to geostatistics to deal with a non-gaussian data distribution and take explicitly into account complex spatial structures (i.e. trends, patchiness and random fluctuations). A common characteristic of animal count data is a distribution that is both zero-inflated and heavy tailed. In such cases, empirical variograms are no more robust and most structural analyses result in poor and noisy estimated spatial variogram structures. Thus kriged maps feature a broad variance of prediction. Moreover, due to the heterogeneity of wildlife population habitats, a nonstationary model is often required. To avoid these difficulties, we propose a hierarchical model that assumes that the count data follow a Poisson distribution given a theoretical sighting density which is a latent variable to be estimate. This density is modelled as the product of a positive long range trend by a positive stationary random field, characterized by a unit mean and a variogram function. A first estimate of the drift is used to obtain an estimate of the variogram of residuals including a correction term for variance coming from the Poisson distribution and weights due to the non-constant spatial mean. Then a kriging procedure similar to a modified universal kriging is implemented to directly map the latent density from raw count data. An application on fin whale data illustrates the effectiveness of the method in mapping animal density in a context that is presumably non-stationar

Seasonal diversity dynamics of a boreal zooplankton community under climate impact

Seasonality and long-term environmental variability afect species diversity through their efects on the dynamics of species. To investigate such efects, we ftted a dynamic and heterogeneous species abundance model generating the lognormal species abundance distribution to an assemblage of freshwater zooplankton sampled fve times a year (June–October) during the ice-free period over 28 years (1990–2017) in Lake Atnsjøen (Norway). By applying a multivariate stochastic community dynamics model for describing the fuctuations in abundances, we show that the community dynamics was driven by environmental variability in spring (i.e., June). In contrast, community-level ecological heterogeneity is highest in autumn. The autumn months (i.e., September and October) that rearranged the community are most likely crucial months to monitor long-term changes in community structure. Indeed, noises from early summer are fltered away, making it easier to track long-term changes. The community returned faster towards equilibrium when ecological heterogeneity was the highest (i.e., in September and October). This occurred because of stronger density-regulation in months with highest ecological heterogeneity. The community responded to the long-term warming of water temperature with decreasing species diversity and increasing abundance. Unevenness associated with variabilities in abundances might afect species interactions within the community. These can have consequences for the stability and functioning of the ecosystem

Seasonal diversity dynamics of a boreal zooplankton community under climate impact

Seasonality and long-term environmental variability afect species diversity through their efects on the dynamics of species. To investigate such efects, we ftted a dynamic and heterogeneous species abundance model generating the lognormal species abundance distribution to an assemblage of freshwater zooplankton sampled fve times a year (June–October) during the ice-free period over 28 years (1990–2017) in Lake Atnsjøen (Norway). By applying a multivariate stochastic community dynamics model for describing the fuctuations in abundances, we show that the community dynamics was driven by environmental variability in spring (i.e., June). In contrast, community-level ecological heterogeneity is highest in autumn. The autumn months (i.e., September and October) that rearranged the community are most likely crucial months to monitor long-term changes in community structure. Indeed, noises from early summer are fltered away, making it easier to track long-term changes. The community returned faster towards equilibrium when ecological heterogeneity was the highest (i.e., in September and October). This occurred because of stronger density-regulation in months with highest ecological heterogeneity. The community responded to the long-term warming of water temperature with decreasing species diversity and increasing abundance. Unevenness associated with variabilities in abundances might afect species interactions within the community. These can have consequences for the stability and functioning of the ecosystem. Freshwater · Lognormal distribution · Similarity · Return to equilibrium · Time-serie