Bruno Carturan

Bruno Carturan
University of British Columbia - Okanagan | UBC Okanagan · Department of Biology

Doctor of Philosophy
I just finished a postdoc at UBC (Okanagan), working on modelling pollination services provided by bumble bees.

About

9
Publications
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25
Citations

Publications

Publications (9)
Article
Full-text available
Within the Anthropocene the functional diversity of coral communities is changing rapidly, putting the resilience of many coral reef ecosystems in jeopardy. A better understanding of the relationship between coral functional diversity and reef resilience could reveal practical ways to achieve increased resilience. However, manipulating coral divers...
Article
Full-text available
The complexity of coral-reef ecosystems makes it challenging to predict their dynamics and resilience under future disturbance regimes. Models for coral-reef dynamics do not adequately account for the high functional diversity exhibited by corals. Models that are ecologically and mechanistically detailed are therefore required to simulate the ecolo...
Article
Full-text available
The complexity of coral-reef ecosystems makes it challenging to predict their dynamics and resilience under future disturbance regimes. Models for coral-reef dynamics do not adequately account for the high functional diversity exhibited by corals. Models that are ecologically and mechanistically detailed are therefore required to simulate the ecolo...
Article
Full-text available
The complexity of coral-reef ecosystems makes it challenging to predict their dynamics and resilience under future disturbance regimes. Models for coral-reef dynamics do not adequately account for the high functional diversity exhibited by corals. Models that are ecologically and mechanistically detailed are therefore required to simulate the ecolo...
Preprint
Full-text available
The complexity of coral-reef ecosystems makes it challenging to predict their dynamics and resilience under future disturbance regimes. Models for coral-reef dynamics do not adequately accounts for the high functional diversity exhibited by corals. Models that are ecologically and mechanistically detailed are therefore required to simulate the ecol...
Article
Full-text available
Axons normally degenerate during development of the mammalian nervous system, but dysregulation of the same genetically-encoded destructive cellular machinery can destroy crucial structures during adult neurodegenerative diseases. Nerve growth factor (NGF) withdrawal from dorsal root ganglia (DRG) axons is a well-established in vitro experimental m...
Article
Full-text available
Climate change and other anthropogenic disturbances are having dramatic impacts on tropical coral reefs and the ecosystem services they provide. Anticipating change in ecosystem services is a pressing challenge that requires understanding how species respond to an environmental change, and how they contribute to ecosystem services. Building on anal...

Questions

Questions (4)
Question
In R, I fitted a full linear model applying varExp variance structure to one of the explanatory variables, such as:
varS <- varExp(form = ~ X1)
model <- gls(Y ~ X1 * X2 * X, data = dataset, weights = varS)
I then used the R package MuMIn to generate multiple models, select the 95% confidence set and averaged the latter, such as:
options(na.action = "na.fail")
model.dredge <- dredge(model, rank = "AICc", beta = "none")
options(na.action = "na.omit")
model.selected.tot <- model.sel(object = model.dredge, beta = F, fit = T)
model.selected <- get.models(model.selected.tot, subset = cumsum(weight) <= .95)
model.av <- model.avg(model.selected.tot, fit = T)
The list model.selected contains models that have different estimates of the coefficient for the variance structure, showing that the latter is accounted for when generating these models.
The object model.av contains the estimates of the coefficient of the linear model (model.av$coefficients) but not of the variance structure. Similarly, the command confint(model.av) provides the mean and 95% confidence interval of the model coefficients but not of the variance structure.
Question: How can I obtain the estimate and confidence interval of the coefficient of the variance structure of the averaged model?
Note that the commande intervals(model) from the package nlme provides the estimate and confidence interval of the coefficient of the variance structure, but the command does not work with averaged model object.
Question
I built a model that outputs the composition of a coral community (i.e., % cover for each species) at different time steps. The rows of my dataset are consecutive time units and the columns are the different species. I want to compare this output to a real dataset (which has in consequence the same dimensions) in order to measure how close to the real data is my output data.
There are many coefficients out there (e.g., RVcoefficient and its different versions, principal response curves, Mantel test, etc.) and I'm confused about which is the one to pick in my case.
Thanks.
Question
I am interested in the implication of the correlation for the resilience of ecosystem functions, not in knowing the source of an eventual correlation (phylogenetic or ecological trade-offs).
Question
I am wondering if I can combine these two traits under the same trait.

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Projects

Projects (2)
Project
The goal is to test the hypothesis that higher functional diversity provides resilience in coral reef communities. Using our coral agent-based model, we conducted an experiment where we manipulated functional diversity in coral reef communities and measured different aspects of their resilience. Results will be out soon!