Avian seed dispersal may be insufficient for plants to track future temperature change on tropical mountains
Cite this dataset
Nowak, Larissa et al. (2022). Avian seed dispersal may be insufficient for plants to track future temperature change on tropical mountains [Dataset]. Dryad. https://doi.org/10.5061/dryad.4f4qrfjdm
Abstract
Abstract
Aim: Climate change causes species’ range shifts globally. Terrestrial plant species often lag behind temperature shifts, and it is unclear to what extent animal-dispersed plants can track climate change. Here, we estimate the ability of bird-dispersed plant species to track future temperature change on a tropical mountain.
Location: Tropical elevational gradient (500–3500 m a.s.l.) in the Manú biosphere reserve, Peru
Time period: 1960–1990 to 2061–2080
Taxa: Fleshy-fruited plants, avian frugivores
Methods: Using simulations based on the functional traits of avian frugivores and fruiting plants, we quantified the number of long-distance dispersal (LDD) events that woody plant species would require to track projected temperature shifts on a tropical mountain by the year 2070 under different greenhouse gas emission scenarios (RCP 2.6, 4.5 and 8.5). We applied this approach to 343 bird-dispersed woody plant species.
Results: Our simulations reveal that bird-dispersed plants differ in their climate-tracking ability, with large-fruited and canopy plants exhibiting a higher climate-tracking ability. Our simulations also suggest that even under scenarios of strong and intermediate mitigation of greenhouse gas emissions (RCP 2.6 and 4.5), sufficient upslope dispersal would require several LDD events by 2070, which is unlikely for the majority of woody plant species. Furthermore, the ability of plant species to track future temperature changes increased in simulations with a low degree of trait matching between plants and birds, suggesting that plants in generalised seed-dispersal systems may be more resilient to climate change.
Main conclusion: Our study illustrates how plant and animal functional traits can inform predictive models of species dispersal and range shifts under climate change and suggests that the biodiversity of tropical mountain ecosystems is highly vulnerable to future warming. The increasing availability of functional trait data for plants and animals globally will allow parameterisation of similar models for many other seed-dispersal systems.
Methods
|
S1 Plant species |
| Minimum and maximum elevation (m a.s.l.) were compiled from Brako, L. & Zarucchi, J.L. (1993). Catalogue of the flowering plants and Gymnosperms of Peru. Catálogo de las Angiospermas y Gimnospermas del Perú. Monogr. Syst. Bot. from Missouri Bot. Gard. 45. |
| Growth form has been defined during field surveys |
| Fruit width (mm) was measured on fruits collected during the field surveys on 20 fruits per plant species; given are species mean values |
| Plant height (m) was measured during the field surveys on all individuals in the plots; given are species' mean values |
|
Source indicates if trait values are species mean values based on data from our own field surveys, species mean values based on data from Ecuador or genus mean values. |
|
S2 Bird species |
|
Minimum and maximum elevation (m a.s.l.) were compiled from Merkord, C.L. (2010). Seasonality and elevational migration in an Andean bird community. University of Missouri-Columbia; Walker, B., Stotz, D.F., Pequeño, T. & Fitzpatrick, J.W. (2006). Birds of the Manu Biosphere Reserve. Fieldiana Zool., 23–49 and complemented by Dehling, D.M., Sevillano, C.S. & Morales, L.V. (2013). Upper and lower elevational extremes of Andean birds from south-east Peru. Boletín Inf., 8, 32–38. |
|
Body mass (g) was compiled from Dunning, J.B. (2007). CRC Handbook of Avian Body Masses. CRC Press, Boca Raton. |
|
Bill width (mm) was measured on museum specimens (at least two adult males and females) according to measurement protocols from Eck, S., Töpfer, T., Fiebig, J., Heynen, I., Fiedler, W., Nicolai, B., et al. (2011). Measuring birds. Christ Media Natur, Minden. |
|
Kipp's index equals the Kipp's distance (mm) divided by the wing length (mm), which were both measured on museum specimens (at least two adult males and females) according to measurement protocols from Eck, S., Töpfer, T., Fiebig, J., Heynen, I., Fiedler, W., Nicolai, B., et al. (2011). Measuring birds. Christ Media Natur, Minden.
S3 Projections |
|
LDD (m) was simulated based on three trait-matching parameter values representing a low, an intermediate and a high degree of trait matching (s = 0.5, 1.5, 5.0, respectively), and estimated as the 95th and the 99th percentile of the simulated 10000 dispersal distances per plant species (95% and 99% LDD ability, respectively); given are mean, standard deviation and coefficient of variation across 10 independent iterations of the simulations. |
|
MAX (m) was simulated based on three trait-matching parameter values representing a low, an intermediate and a high degree of trait matching (s = 0.5, 1.5, 5.0, respectively), and was estimated as the maximum of the simulated 10000 dispersal distances per plant species; given are the mean and standard deviation across 10 independent iterations of the simulations. |
|
Vertical temperature shift (m) by 2070 was projected according to three future greenhouse gas emission scenarios (RCP 2.6, 4.5 and 8.5) and five general circulation models (GCMs; cc = CCSM4, he = HadGEM2-ES, mc = MIROC 5, mg = MRI-CGCM and no = NorESM1-M). Data on current and projected mean annual temperature along the Manú gradient was derived from Worldclim (Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol., 25, 1965–1978). Data on tropospheric lapse rate for our study regions was compiled from Mokhov, I.I. & Akperov, M.G. (2006). Tropospheric lapse rate and its relation to surface temperature from reanalysis data. Izv. Atmos. Ocean. Phys., 42, 430–438. Given are mean and standard deviation of the projected vertical distances across species' elevational range as well as mean and standard deviation across general circulation models. |
|
Required horizontal dispersal distance (m) considering an average slope of 11.45° is computed as vertical temperature shift (m) divided by the sine of the mean slope (°). This approximates the distance by which a plant species would have to disperse horizontally to shift its range upslope by a given vertical distance considering a mean slope of 11.45 °. |
| Number of required LDD events by 2070 equals the projected number of LDD events plant species' would require to fully track projected vertical temperature shifts until 2070. This was computed as LDD ability (m)/ Required horizontal dispersal distance (m) for each plant species. |