Climate Change and Human Disturbance Can Lead to Local Extinction of Alpine Rock Ptarmigan: New Insight from the Western Italian Alps

<div><p>Alpine grouses are particularly vulnerable to climate change due to their adaptation to extreme conditions and to their relict distributions in the Alps where global warming has been particularly marked in the last half century. Grouses are also currently threatened by habitat modification and human disturbance, and an assessment of the impact of multiple stressors is needed to predict the fate of Alpine populations of these birds in the next decades. We estimated the effect of climate change and human disturbance on a rock ptarmigan population living in the western Italian Alps by combining an empirical population modelling approach and stochastic simulations of the population dynamics under the a1B climate scenario and two different disturbance scenarios, represented by the development of a ski resort, through 2050.The early appearance of snow-free ground in the previous spring had a favorable effect on the rock ptarmigan population, probably through a higher reproductive success. On the contrary, delayed snowfall in autumn had a negative effect possibly due to a mismatch in time to molt to white winter plumage which increases predation risk. The regional climate model PROTHEUS does not foresee any significant change in snowmelt date in the study area, while the start date of continuous snow cover is expected to be significantly delayed. The net effect in the stochastic projections is a more or less pronounced (depending on the model used) decline in the studied population. The addition of extra-mortality due to collision with ski-lift wires led the population to fatal consequences in most projections. Should these results be confirmed by larger studies the conservation of Alpine populations would deserve more attention. To counterbalance the effects of climate change, the reduction of all causes of death should be pursued, through a strict preservation of the habitats in the present area of occurrence.</p> </div

Climatic effects on rock ptarmigan population dynamics.

<p>Relationship between the population growth rate and snowmelt date at time <i>t-1</i> (a) (days from 1^st May) and start date of a continuous snow cover at time <i>t-1</i> (b) (days from 1^st October).</p

Time series of population densities.

<p>Observed spring cock densities in the period 1996-2012 (dots and solid line) and estimated breeding pairs densities from the fitted Gompertz state-space model (triangles and dotted line).</p

Population projections of rock ptarmigans for the period 2013-2050.

<p>(a) Projections performed using populations models M1, M2, and M6 (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081598#pone-0081598-t001" target="_blank">Table 1</a>) or a simple Gompertz density dependence model (DD) and the meteorological variables generated by the PROTHEUS model for the A1B scenario; (b) simulations of the joint effect of climate change and human disturbance using the same models as before and an extra-mortality term due to wire collision in a highly developed ski resort (0.16 <i>N</i>_<i>t-1</i> + 0.02 individuals/km² per year). Thick line: estimated breeding pairs densities (cocks/km²); thin line: 50% percentile, shaded area: 5–95% percentiles of the 1000 runs; the red line represents one random realization.</p

Appendix E. Aggregate vital rate data.

Aggregate vital rate data

Appendix A. Census data for the Alpine ibex population (Gran Paradiso National Park, Italy) and corresponding climate data (snow depth, temperature, and precipitation).

Census data for the Alpine ibex population (Gran Paradiso National Park, Italy) and corresponding climate data (snow depth, temperature, and precipitation)

Appendix B. Results of diagnostic tests on model residuals.

Results of diagnostic tests on model residuals

Appendix C. Correlations of changes in total fall ibex count with climate variables.

Correlations of changes in total fall ibex count with climate variables

Appendix D. Model equations.

Model equations

Table1_Non-steady-state closed dynamic chamber to measure soil CO2 respiration: A protocol to reduce uncertainty.XLSX

Non-steady-state closed dynamic accumulation chambers are widely used to measure the respiration of terrestrial ecosystems, thanks to their low cost, low energy consumption and simple transportability, that allow measurements even in hostile and remote environments. However, the assessment of the accuracy and precision associated with the measurement system (independently of possible disturbances due to chamber-soil interactions) is rarely reported. This information is instead necessary for basic quality control, to compare data obtained by different devices and regression models and to provide Confidence Intervals (CIs) on the carbon flux values. This study quantifies the uncertainty associated with emission flux measurements, with a focus on very low fluxes. Calibration tests using different accumulation chambers and CO2 sensors were performed, and fluxes were calculated by means of different models (parametric, non-parametric and flux models). The results of this work show that the linear regression model has the best reproducibility when compared to the other tested models, regardless of the sensor used and the chamber volumes, while the second order polynomial regression has the best accuracy. We remark the importance of building a calibration curve in the range of the expected flux values, with an interval between the lowest and highest imposed flux that should not exceed two orders of magnitude. To evaluate the reproducibility of the measurement, performing replicates for each imposed flux value is essential. We also show that it is necessary to carefully identify the best time interval for interpolating the CO2 concentration curve in order to guarantee reproducibility and accuracy in flux estimates.</p