Carnicer, J., Wheat, C., Vives, M., Ubach, A., Domingo, C., Nylin, S., Stefanescu, C., Vila, R., Wiklund, C., Peñuelas, J. (2016) Evolutionary responses of invertebrates to global climate change: The role of life-history trade-offs and multidecadal climate shifts. Global Climate Change and Terrestrial Invertebrates. : 319-348.EnlaceDoi: 10.1002/9781119070894.ch16
Peñuelas, J., Sardans, J., Filella, I., Estiarte, M., Llusià, J., Ogaya, R., Carnicer, J., Bartrons, M., Rivas-Ubach, A., Grau, O., Peguero, G., Margalef, O., Pla-Rabés, S., Stefanescu, C., Asensio, D., Preece, C., Liu, L., Verger, A., Rico, L., Barbeta, A., Achotegui-Castells, A., Gargallo-Garriga, A., Sperlich, D., Farré-Armengol, G., Fernández-Martínez, M., Liu, D., Zhang, C., Urbina, I., Camino, M., Vives, M., Nadal-Sala, D., Sabaté, S., Gracia, C., Terradas, J. (2016) Assessment of the impacts of climate change on Mediterranean terrestrial ecosystems based on data from field experiments and long-term monitored field gradients in Catalonia. Environmental and Experimental Botany. : 0-0.EnlaceDoi: 10.1016/j.envexpbot.2017.05.012
Sardans J., Alonso R., Carnicer J., Fernández-Martínez M., Vivanco M.G., Peñuelas J. (2016) Factors influencing the foliar elemental composition and stoichiometry in forest trees in Spain. Perspectives in Plant Ecology, Evolution and Systematics. 18: 52-69.EnlaceDoi: 10.1016/j.ppees.2016.01.001
Concentrations of nutrient elements in organisms and in the abiotic environment are key factors influencing ecosystem structure and function. We studied how concentrations and stoichiometries of nitrogen (N), phosphorus (P) and potassium (K) in leaves of forest trees are related to phylogeny and to environmental factors (mean annual precipitation, mean annual temperature, forest type, and nitrogen deposition). Using data for 4691 forest plots from across Spain, we tested the following hypotheses: (i) that foliar stoichiometries of forest trees are strongly influenced by phylogeny, (ii) that climate, as an important driver of plant uptake and nutrient use efficiency, affects foliar stoichiometry, (iii) that long-term loads of N influence N, P and K concentrations and ratios in natural vegetation, and (iv) that sympatric species are differentiated according to their foliar stoichiometry, thereby reducing the intensity of resource competition. Our analyses revealed that several factors contributed to interspecific variation in elemental composition and stoichiometry. These included phylogeny, forest type, climate, N deposition, and competitive neighborhood relationships (probably related to niche segregation effect).These findings support the notion that foliar elemental composition reflects adaptation both to regional factors such as climate and to local factors such as competition with co-occurring species. © 2016 Elsevier GmbH.
Schmucki R., Pe'er G., Roy D.B., Stefanescu C., Van Swaay C.A.M., Oliver T.H., Kuussaari M., Van Strien A.J., Ries L., Settele J., Musche M., Carnicer J., Schweiger O., Brereton T.M., Harpke A., Heliölä J., Kühn E., Julliard R. (2016) A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes. Journal of Applied Ecology. 53: 501-510.EnlaceDoi: 10.1111/1365-2664.12561
The rapid expansion of systematic monitoring schemes necessitates robust methods to reliably assess species' status and trends. Insect monitoring poses a challenge where there are strong seasonal patterns, requiring repeated counts to reliably assess abundance. Butterfly monitoring schemes (BMSs) operate in an increasing number of countries with broadly the same methodology, yet they differ in their observation frequency and in the methods used to compute annual abundance indices. Using simulated and observed data, we performed an extensive comparison of two approaches used to derive abundance indices from count data collected via BMS, under a range of sampling frequencies. Linear interpolation is most commonly used to estimate abundance indices from seasonal count series. A second method, hereafter the regional generalized additive model (GAM), fits a GAM to repeated counts within sites across a climatic region. For the two methods, we estimated bias in abundance indices and the statistical power for detecting trends, given different proportions of missing counts. We also compared the accuracy of trend estimates using systematically degraded observed counts of the Gatekeeper Pyronia tithonus (Linnaeus 1767). The regional GAM method generally outperforms the linear interpolation method. When the proportion of missing counts increased beyond 50%, indices derived via the linear interpolation method showed substantially higher estimation error as well as clear biases, in comparison to the regional GAM method. The regional GAM method also showed higher power to detect trends when the proportion of missing counts was substantial. Synthesis and applications. Monitoring offers invaluable data to support conservation policy and management, but requires robust analysis approaches and guidance for new and expanding schemes. Based on our findings, we recommend the regional generalized additive model approach when conducting integrative analyses across schemes, or when analysing scheme data with reduced sampling efforts. This method enables existing schemes to be expanded or new schemes to be developed with reduced within-year sampling frequency, as well as affording options to adapt protocols to more efficiently assess species status and trends across large geographical scales. Monitoring offers invaluable data to support conservation policy and management, but requires robust analysis approaches and guidance for new and expanding schemes. Based on our findings, we recommend the regional generalized additive model approach when conducting integrative analyses across schemes, or when analysing scheme data with reduced sampling efforts. This method enables existing schemes to be expanded or new schemes to be developed with reduced within-year sampling frequency, as well as affording options to adapt protocols to more efficiently assess species status and trends across large geographical scales. Journal of Applied Ecology © 2016 British Ecological Society.
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