Species traits are thought to predict feeding specialisation and the vulnerability of a species to extinctions of interaction partners, but the context in which a species evolved and currently inhabits may also matter. Notably, the predictive power of traits may require that traits evolved to fit interaction partners. Furthermore, local abiotic and biotic conditions may be important. On islands, for instance, specialised and vulnerable species are predicted to be found mainly in mountains, whereas species in lowlands should be generalized and less vulnerable. We evaluated these predictions for hummingbirds and their nectar-food plants on Antillean islands. Our results suggest that the rates of hummingbird trait divergence were higher among ancestral mainland forms before the colonization of the Antilles. In correspondence with the limited trait evolution that occurred within the Antilles, local abiotic and biotic conditions – not species traits – correlate with hummingbird resource specialisation and the vulnerability of hummingbirds to extinctions of their floral resources. Specifically, hummingbirds were more specialised and vulnerable in conditions with high topographical complexity, high rainfall, low temperatures, and high floral resource richness, which characterize the Antillean Mountains. These findings show that resource specialisation and species vulnerability to extinctions of interaction partners are highly context dependent.
Data_and_code
To run the extinction models, first download the Stochastic Coextinction Model code from the Supplementary Information of Vieira and Almeida-Neto (2015) (see references). The script netcascade (April 2014).R is required for the analyses in this paper.
The supplementary information contains three scripts:
1. Main.R – This script runs simulations on the 8 networks to calculate PE, SE and VE for each species in each network.
2. IterativeNodeDeletion.R – A wrapper around netcascade (April 2014).R that calls netcascade() repeatedly to iteratively remove species from the network.
3. IterNodeDelMultiSim.R – A wrapper around IterativeNodeDeletion.R that runs IterativeNodeDeletion() multiple times to account for the stochasticity in the netcascade() algorithm.
And four datasets:
1. data/rvalues.csv – The expert-assigned dependency categories for each plant species in each network used to determine R values
2. data/webs – Folder containing the 8 interaction matrices
3. data/phylogeny - contains (1) a set of 1000 phylogenies for the 13 Caribbean hummingbird species that were analysed (Caribbean_Phylogenies_.nex), (2) the maximum clade credibility (MCC) phylogeny (Caribbean_MCC_Phylogeny.nex)
4. data/traits - species-level data for body mass and bill length
If you use the extinction modelling code please cite the following two papers:
1. Dalsgaard et al. (in press) Trait evolution, resource specialisation and vulnerability to plant extinctions among Antillean hummingbirds. Proceedings of the Royal Society B.
2. Vieira, M.C. and Almeida‐Neto, M., 2015. A simple stochastic model for complex coextinctions in mutualistic networks: robustness decreases with connectance. Ecology Letters, 18(2), pp.144-152. doi: 10.1111/ele.12394
If you use the data please cite:
Dalsgaard et al. (in press) Trait evolution, resource specialisation and vulnerability to plant extinctions among Antillean hummingbirds. Proceedings of the Royal Society B.
References:
Vieira, M.C. and Almeida‐Neto, M., 2015. A simple stochastic model for complex coextinctions in mutualistic networks: robustness decreases with connectance. Ecology Letters, 18(2), pp.144-152. doi: 10.1111/ele.12394
