João Manuel Cordeiro Vale PereiraUniversity of Freiburg | Albert-Ludwigs-Universität Freiburg · Chair in Wildlife Ecology and Management
João Manuel Cordeiro Vale Pereira
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Citations since 2017
8 Research Items
My broad research area is applied conservation ecology of birds, namely on forest ecosystems (both temperate and tropical). I am interested in the topics of habitat restoration, trophic ecology and landscape ecology. I am additionally interested in public outreach regarding biodiversity conservation and in particular for birds. I am now working on a PhD at Freiburg university, looking at the relationship between bird assemblages and forest management practices in SW Germany.
July 2019 - December 2020
- PhD Student
- Part of DFG-funded Research Training Group ConFoBi (Conservation of Forest Biodiversity in Multiple-use Landscapes of Central Europe): https://confobi.uni-freiburg.de/en Responsible for sub-project B6 (forest-bird interactions), focusing on the role of food supply and trophic interactions in the link between forest structure and forest bird abundance and diversity.
January 2016 - July 2016
Companhia das Lezírias
- Nature guide
- Guided essentially school visits but visits with the general public as well. Besides bird identification and transmitting knowledge about the biology of wetland birds, an important component of these visits was to raise awareness about the uniqueness and ecological value of this wetland area , the most important one in the country and one of the most important ones in the East Atlantic Flyway.
July 2014 - August 2014
SPEA - Portuguese Society for the Study of Birds
- Conservation Intern
- 41-day conservation traineeship in the context of LIFE+ Berlengas Project, at Berlengas Islands Nature Reserve, Portugal. Main tasks were a) monitoring of seabird nests during breeding period, including monitoring of egg predation with camera traps and measuring of chicks, b) monitoring of offshore seabird movements, c) vegetation surveys aimed at island endemics, c) removal of invasive plant species, d) survey of black rat population, using mark and recapture methods.
The European biodiversity and forest strategies rely on forest sustainable management (SFM) to conserve forest biodiversity. However, current sustainability assessments hardly account for direct biodiversity indicators. We focused on forest multi-taxon biodiversity to: i) gather and map the existing information; ii) identify knowledge and research...
Manually annotating audio files for bird species richness estimation or machine learning validation is a time‐intensive task. A premium is placed on the subselection of files that will maximize the efficiency of unique additional species identified, to be used for future analyses. Using acoustic data collected in 17 plots, we created 60 subsetting...
Background Species co-occurrences can have profound effects on the habitat use of species, and therefore habitat structure alone cannot fully explain observed abundances. To account for this aspect of community organization, we developed multi-species abundance models, incorporating the local effect of co-occurring and potentially associated specie...
The IUCN's Red List categories are used as a guideline by many governments and other organizations to assess the conservation status of species, to develop laws for species conservation, and to set conservation priorities such as prioritized funding. Yet, the IUCN categorization of species is often in discrepancy with the actual conservation status...
Species associations can have profound effects on the realized habitat niche of species, indicating that habitat structure alone cannot fully explain observed abundances. To account for this aspect of community organization in niche modelling, we developed multi-species abundance models, incorporating the local effect of potentially associated spec...
I am doing a characterization of the bird community associated to a specific habitat (stands of Puya raimondii in the High Andes), and, besides an estimation of richness, I want to describe in a broader sense the alpha diversity of the community. This normally needs to take into account relative abundances of species (with Shannon index, for instance), and I am wondering if I could use replicated presence/absence data as a representation of relative abundances. I had 35 replicates within this habitat, where I assessed which bird species were present. Is it legitimate to use the number of times a species shows up throughout the replicates as a proxy for its abundance in relation to others?
I have a dataset of about 100 bird surveys carried out in high-altitude ecosystems in the Andes for my Master's thesis. At this stage of the analysis, I'm planning to use GLMM's to look at the abundance of each among a few selected species, as response variable, and a number of environmental variables as explanatory variables. I have opted for GLMM's instead of regular GLM's since there are nested groupings of non-independent data in my counts: there were eight woodland patches covered, each countaining/near 5 to 10 survey points, and each survey point was surveyed twice, at different times of the day. In this case, survey point and patch would be random effect variables in the GLMM.
I'm having more trouble, however, in thinking how to model the fixed effects. Some of them (e.g. time of the day, weather condition) are specific to each survey, some of them (e.g. most vegetation/habitat variables) are specific to each point, and some of them (e.g. patch area, patch connectivity) are measured per patch. What would good practice be in writing a model in this case?
I'm conducting analysis of bird counts for my Master's thesis on effects of patch size and connectivity on birds of High Andean landscapes. My first goal is to use ordination analysis to figure out which bird species are associated to each of the different kinds of habitat (forest, transitional and open matrix). I have lots of environmental/spatial variables recorded, but I decided to begin with an unconstrained ordination, just labeling the sites with different colours according to habitat and checking which sites and which species seem to group together.
My data is not very good (for many reasons, one of them just not having had enough time in the field) but I'm trying to salvage it the best I can. I've ran a CA and a DCA on my species matrix, using vegan package in R, and the procrustes function shows me large (and quite chaotic) differences between the plots from one method and the other. Is this telling me that arch effects or compression of extreme scores is happening with the CA, and so I should opt for the DCA? Or is it just because the CA explains very little variation in the data (the first two axis amount to around 18% of total inertia), so sites and species will just float around with no real meaning when I do the DCA?
A little extra question - would it help me to get more variation explained if I remove from my dataset some of the rarest species or some of the ones that move around the most between the CA and the DCA?