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共检索到22条 ,权限内显示50条;
Data from: A tale of two phylogenies: comparative analyses of ecological interactions
- 负责人：
- DOI：
- doi:10.5061/dryad.jf3tj
- 摘要：
- kely to be shaped by the phylogenetic history of both parties. We develop generalized linear mixed-effects models (GLMM) that estimate the effect of both parties
Data from: When the going gets tough: behavioral type dependent space use in the sleepy lizard changes as the season dries
- 负责人：
- 关键词：
- animal personality Bayesian GLMM Behavioral syndromes GPS-telemetry Movement ecology Spatial ecology
- DOI：
- doi:10.5061/dryad.h4dt0
- 摘要：
- generalized linear mixed models (GLMM) showed that lizards responded to the spatial distribution of resources at the neighbourhood scale and to the intensity of space use by other
Data from: Improving estimates of environmental change using multilevel regression models of Ellenberg indicator values
- 负责人：
- 关键词：
- DOI：
- doi:10.5061/dryad.r8k26cd
- 摘要：
- multilevel model performance to GLMM’s fitted to point estimates of site mean EIVs. We also test the reliability of this method to improve inferences with incomplete
Data from: R2s for correlated data: phylogenetic models, LMMs, and GLMMs
- 负责人：
- DOI：
- doi:10.5061/dryad.345v6
- 摘要：
- of the ordinary least-squares R2 that weights residuals by variances and covariances estimated by the model; it is closely related to R2glmm presented by Nakagawa
Data from: Species divergence and maintenance of species cohesion of three closely related Primula species in the Qinghai-Tibet Plateau
- 负责人：
- DOI：
- doi:10.5061/dryad.tt8n46q
- 摘要：
- , generalized linear mixed modeling (GLMM) and niche-based species distribution modeling (SDM). Results: The three species are clearly delimited by the RADseq data. Further
Data from: Very high resolution digital elevation models: are multi-scale derived variables ecologically relevant?
- 负责人：
- DOI：
- doi:10.5061/dryad.43md3
- 摘要：
- e assessed with multivariate Generalized Linear Models (GLM) and Mixed Models (GLMM). Specific VHR DEM-derived variables showed significant associations
Data from: A trait-based framework for understanding predator-prey relationships: trait matching between a specialist snake and its insect prey
- 负责人：
- DOI：
- doi:10.5061/dryad.5nj70ks
- 摘要：
- ween predators and prey. 2. Here we develop a novel analytical approach based on generalized linear mixed-effects models (GLMM) to test the importance
Data from: Differential response to heat-stress among evolutionary lineages of an aquatic invertebrate species complex
- 负责人：
- DOI：
- doi:10.5061/dryad.7s11m96
- 摘要：
- Under global warming scenarios, rising temperatures can constitute heat stress to which species may differentially respond. Within a describe
Data from: Linking Avicennia germinans (Acanthaceae) architecture to gall richness and abundance in Brazilian Amazon mangroves
- 负责人：
- DOI：
- doi:10.5061/dryad.rt8f0
- 摘要：
- , is considered to be a superhost for gall-inducing insects. Using a Generalized Linear Mixed Model (GLMM) based on the analysis of 1000 apical branches from 50
Data from: Integrating multiple technologies to understand the foraging behaviour of Hawaiian monk seals
- 负责人：
- Wilson, Kenady
- DOI：
- doi:10.5061/dryad.s0b80
- 摘要：
- The objective of this research was to investigate and describe the foraging behaviour of monk seals in the main Hawaiian Islands. Specifically, our goal was to identify a metric to classify foraging behaviour from telemetry instruments. We deployed accelerometers, seal-mounted cameras and GPS tags on six monk seals during 2012–2014 on the islands of Molokai, Kauai and Oahu. We used pitch, calculated from the accelerometer, to identify search events and thus classify foraging dives. A search event and consequent ‘foraging dive’ occurred when the pitch was greater than or equal to 70° at a depth less than or equal to ?3?m. By integrating data from the accelerometers with video and GPS, we were able to ground-truth this classification method and identify environmental variables associated with each foraging dive. We used Bayesian logistic regression to identify the variables that influenced search events. Dive depth, body motion (mean overall dynamic body acceleration during the dive) and proximity to the sea floor were the best predictors of search events for these seals. Search events typically occurred on long, deep dives, with more time spent at the bottom (more than 50% bottom time). We can now identify where monk seals are foraging in the main Hawaiian Islands (MHI) and what covariates influence foraging behaviour in this region. This increased understanding will inform management strategies and supplement outreach and recovery efforts.
