A multistate dynamic site occupancy model for spatially aggregated sessile communities

Markov community models have been applied to sessile organisms because such models facilitate estimation of transition probabilities by tracking species occupancy at many fixed observation points over multiple periods of time. Estimation of transition probabilities of sessile communities seems easy in principle but may still be difficult in practice because resampling error (i.e., a failure to resample exactly the same location at fixed points) may cause significant estimation bias. Previous studies have developed novel analytical methods to correct for this estimation bias. However, they did not consider the local structure of community composition induced by the aggregated distribution of organisms that is typically observed in sessile assemblages and is very likely to affect observations. In this study, we developed a multistate dynamic site occupancy model to estimate transition probabilities that accounts for resampling errors associated with local community structure. The model applies a nonparametric multivariate kernel smoothing methodology to the latent occupancy component to estimate the local state composition near each observation point, which is assumed to determine the probability distribution of data conditional on the occurrence of resampling error. By using computer simulations, we confirmed that an observation process that depends on local community structure may bias inferences about transition probabilities. By applying the proposed model to a real dataset of intertidal sessile communities, we also showed that estimates of transition probabilities and of the properties of community dynamics may differ considerably when spatial dependence is taken into account. Our approach can even accommodate an anisotropic spatial correlation of species composition, and may serve as a basis for inferring complex nonlinear ecological dynamics.Comment: 14 pages, 3 figure

Generalized estimators of avian abundance from count survey data

I consider modeling avian abundance from spatially referenced bird count data collected according to common protocols such as capture-recapture, multiple observer, removal sampling and simple point counts. Small sample sizes and large numbers of parameters have motivated many analyses that disregard the spatial indexing of the data, and thus do not provide an adequate treatment of spatial structure. I describe a general framework for modeling spatially replicated data that regards local abundance as a random process, motivated by the view that the set of spatially referenced local populations (at the sample locations) constitute a metapopulation. Under this view, attention can be focused on developing a model for the variation in local abundance independent of the sampling protocol being considered. The metapopulation model structure, when combined with the data generating model, define a simple hierarchical model that can be analyzed using conventional methods. The proposed modeling framework is completely general in the sense that broad classes of metapopulation models may be considered, site level covariates on detection and abundance may be considered, and estimates of abundance and related quantities may be obtained for sample locations, groups of locations, unsampled locations. Two brief examples are given, the first involving simple point counts, and the second based on temporary removal counts. Extension of these models to open systems is briefly discussed

P2X receptor trafficking in neurons is subunit specific

P2X receptors within the CNS mediate excitatory synaptic transmission and also act presynaptically to modulate neurotransmitter release. We have studied the targeting and trafficking of P2X4 and P2X2 receptors heterologously expressed in cultured olfactory bulb neurons. Homomeric P2X4 receptors had a punctate distribution, and many of the puncta colocalized with early endosomes. In contrast, P2X2 receptors were primarily localized at the plasma membrane. By antibody-labeling of surface receptors in living neurons, we showed that P2X4 receptors undergo rapid constitutive internalization and subsequent reinsertion into the plasma membrane, whereas P2X2 receptors were not regulated in such a way. The internalization of P2X4 receptors was dynamin-dependent, and the binding of ATP enhanced the basal rate of retrieval in a Ca2+-independent manner. The presence of the P2X4 subunit in a P2X4/6 heteromer governed the trafficking properties of the receptor. P2X receptors acted presynaptically to enhance the release of glutamate, suggesting that the regulated cycling of P2X4-containing receptors might provide a mechanism for modulation of synaptic transmission

Estimadores generalizados de abundancia en aves a partir de datos de estudios de recuento

I consider modeling avian abundance from spatially referenced bird count data collected according to common protocols such as capture–recapture, multiple observer, removal sampling and simple point counts. Small sample sizes and large numbers of parameters have motivated many analyses that disregard the spatial indexing of the data, and thus do not provide an adequate treatment of spatial structure. I describe a general framework for modeling spatially replicated data that regards local abundance as a random process, motivated by the view that the set of spatially referenced local populations (at the sample locations) constitute a metapopulation. Under this view, attention can be focused on developing a model for the variation in local abundance independent of the sampling protocol being considered. The metapopulation model structure, when combined with the data generating model, define a simple hierarchical model that can be analyzed using conventional methods. The proposed modeling framework is completely general in the sense that broad classes of metapopulation models may be considered, site level covariates on detection and abundance may be considered, and estimates of abundance and related quantities may be obtained for sample locations, groups of locations, unsampled locations. Two brief examples are given, the first involving simple point counts, and the second based on temporary removal counts. Extension of these models to open systems is briefly discussed.En el presente estudio se analiza la modelación de la abundancia en aves mediante datos de recuento de aves, referenciados espacialmente y obtenidos a partir de protocolos comunes, como los de captura–recaptura, muestreo por observadores múltiples, muestreo por eliminación y recuentos de puntos simples. Las muestras de pequeño tamaño, así como el amplio número de parámetros, han propiciado numerosos análisis que no tienen en cuenta la indexación espacial de los datos y, por consiguiente, no proporcionan un tratamiento adecuado de la estructura espacial. En este trabajo se describe un marco general para la modelación de datos replicados en el espacio, que considera la abundancia local como un proceso aleatorio, todo ello basado en el punto de vista de que el conjunto de poblaciones locales referenciadas espacialmente (en los lugares de toma de muestras) constituye una metapoblación. De este modo, la atención puede centrarse en el desarrollo de un modelo para la variación en la abundancia local que sea independiente del protocolo de muestreo que se esté utilizando. La estructura del modelo metapoblacional, en combinación con el modelo de generación de datos, define un modelo jerárquico simple que puede analizarse mediante el empleo de métodos convencionales. El marco de modelación propuesto es de carácter general, en el sentido de que permite considerar amplias clases de modelos metapoblacionales, covariantes del nivel del emplazamiento sobre datos de detección, y la abundancia, pudiendo obtenerse estimaciones de abundancia y cantidades relacionadas para emplazamientos de muestreo, grupos de emplazamientos y emplazamientos no muestreados. A tal efecto, se incluyen dos breves ejemplos; el primero trata de los recuentos de puntos simples, mientras que el segundo se basa en los recuentos por extracción temporal. También se apunta la posibilidad de ampliar estos modelos a sistemas abiertos

Behavioral plasticity and G × E of reproductive tactics in Nicrophorus vespilloides burying beetles

Journal Article© 2015 The Author(s). Evolution published by Wiley Periodicals, Inc. on behalf of The Society for the Study of Evolution.Phenotypic plasticity is important in the evolution of traits and facilitates adaptation to rapid environmental changes. However, variation in plasticity at the individual level, and the heritable basis underlying this plasticity is rarely quantified for behavioral traits. Alternative behavioral reproductive tactics are key components of mating systems but are not often considered within a phenotypic plasticity framework (i.e., as reaction norms). Here, using lines artificially selected for repeated mating rate, we test for genetic (G × E) sources of variation in reproductive behavior of male Nicrophorus vespilloides burying beetles (including signaling behavior), as well as the role of individual body size, in responsiveness to changes in social environment. The results show that body size influences the response of individuals' signaling behavior to changes in the social environment. Moreover, there was G × E underlying the responses of males to variation in the quality of social environment experienced (relative size of focal male compared to his rival). This shows that individual variation in plasticity and social sensitivity of signaling behavior can evolve in response to selection on investment in mating behavior, with males selected for high mating investment having greater social sensitivity.NER

Multiresolution Models for Nonstationary Spatial Covariance Functions

This is the pre-print version of the article found in Statistical Modelling (http://smj.sagepub.com/).Many geophysical and environmental problems depend on estimating a spatial process that has nonstationary structure. A nonstationary model is proposed based on the spatial field being a linear combination of a multiresolution (wavelet) basis functions and random coefficients. The key is to allow for a limited some number of correlations among coefficients and also to use a wavelet basis that is smooth. When approximately 6 % nonzero correlations are enforced, this representation gives a good approximation to a family of Matern covariance functions. This sparseness is important not only for model parsimony but also has implications for the efficient analysis of large spatial data sets. The covariance model is successfully applied to ozone model output and results in a nonstationary but smooth estimate.This work was supported by National Science Foundation grants DMS-93122686 and DMS-9815344.This work was supported by National Science Foundation grants DMS-93122686 and DMS-9815344

FerriTag is a new genetically-encoded inducible tag for correlative light-electron microscopy

A current challenge is to develop tags to precisely visualize proteins in cells by light and electron microscopy. Here, we introduce FerriTag, a genetically-encoded chemically-inducible tag for correlative light-electron microscopy. FerriTag is a fluorescent recombinant electron-dense ferritin particle that can be attached to a protein-of-interest using rapamycin-induced heterodimerization. We demonstrate the utility of FerriTag for correlative light-electron microscopy by labeling proteins associated with various intracellular structures including mitochondria, plasma membrane, and clathrin-coated pits and vesicles. FerriTagging has a good signal-to-noise ratio and a labeling resolution of approximately 10 nm. We demonstrate how FerriTagging allows nanoscale mapping of protein location relative to a subcellular structure, and use it to detail the distribution and conformation of huntingtin-interacting protein 1 related (HIP1R) in and around clathrin-coated pits