Beyond climate envelopes: effects of weather on regional population trends in butterflies

Although the effects of climate change on biodiversity are increasingly evident by the shifts in species ranges across taxonomical groups, the underlying mechanisms affecting individual species are still poorly understood. The power of climate envelopes to predict future ranges has been seriously questioned in recent studies. Amongst others, an improved understanding of the effects of current weather on population trends is required. We analysed the relation between butterfly abundance and the weather experienced during the life cycle for successive years using data collected within the framework of the Dutch Butterfly Monitoring Scheme for 40 species over a 15-year period and corresponding climate data. Both average and extreme temperature and precipitation events were identified, and multiple regression was applied to explain annual changes in population indices. Significant weather effects were obtained for 39 species, with the most frequent effects associated with temperature. However, positive density-dependence suggested climatic independent trends in at least 12 species. Validation of the short-term predictions revealed a good potential for climate-based predictions of population trends in 20 species. Nevertheless, data from the warm and dry year of 2003 indicate that negative effects of climatic extremes are generally underestimated for habitat specialists in drought-susceptible habitats, whereas generalists remain unaffected. Further climatic warming is expected to influence the trends of 13 species, leading to an improvement for nine species, but a continued decline in the majority of species. Expectations from climate envelope models overestimate the positive effects of climate change in northwestern Europe. Our results underline the challenge to include population trends in predicting range shifts in response to climate change

Representatie van de seizoens hydrologische cyclus in klimaatmodellen voor West-Europa

Abstract niet beschikbaarThis report is the final report of the project "Representation of the Seasonal Hydrological Cycle in climate and Weather Prediction models in west Europe (NRP project 951246)". The report describes the methodology used to unravel the influences of the land surface on the atmosphere, in particular rainfall and temperature. 1-Dimensional SVAT models are tested against observations and new parameterizations for soil hydrology and canopy atmosphere exchange are developed and implemented in the land surface model of a Regional Atmospheric Climate Model (RAMCO). New maps of soil and vegetation properties are developed and used in sensitivity studies. These studies show effects of these new parameters on soil moisture storage and timing of evaporation. Europe-wide these differences are small, but regionally they appear important. Also an unexpectedly close relation between soil moisture, evaporation and rainfall was found in the weather of Europe.SG-NO

Toward a combined seasonal weather and crop productivity forecasting system: Determination of the working spatial scale

A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r(2) = 0.62 (significance level p < 10(-4)) and a negative correlation with r(2) = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (similar to 300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r(2) = 0.53, p < 10(-4)), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r(2) = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data

Modelling risk adaptation and mitigation behaviour under different climate change scenarios

The main objective of this study is to simulate household choice behavior under varying climate change scenarios using choice experiments. Economic welfare measures are derived for society’s willingness to pay (WTP) to reduce climate change induced flood risks through private insurance and willingness to accept compensation (WTAC) for controlled flooding under varying future risk exposure levels. Material flood damage and loss of life are covered in the insurance policy experiment, while the WTAC experiment also captures the economic value of immaterial flood damage such as feelings of discomfort, fear and social disruption. The results show that WTP and WTAC are substantial, suggesting a more prominent role of external social damage costs in cost-benefit analysis of climate change and flood mitigation policies