Bayesian analysis of climate change impacts in phenology

The identification of changes in observational data relating to the climate change hypothesis remains a topic of paramount importance. In particular, scientifically sound and rigorous methods for detecting changes are urgently needed. In this paper, we develop a Bayesian approach to nonparametric function estimation. The method is applied to blossom time series of Prunus avium L., Galanthus nivalis L. and Tilia platyphyllos SCOP. The functional behavior of these series is represented by three different models: the constant model, the linear model and the one change point model. The one change point model turns out to be the preferred one in all three data sets with considerable discrimination of the other alternatives. In addition to the functional behavior, rates of change in terms of days per year were also calculated. We obtain also uncertainty margins for both function estimates and rates of change. Our results provide a quantitative representation of what was previously inferred from the same data by less involved methods.     

Climate warming,ecological mismatch at arrival and population decline in migratory birds

Climate is changing at a fast pace, causing widespread, profound consequences for living organisms. Failure to adjust the timing of life-cycle events to climate may jeopardize populations by causing ecological mismatches to the life cycle of other species and abiotic factors. Population declines of some migratory birds breeding in Europe have been suggested to depend on their inability to adjust migration phenology so as to keep track of advancement of spring events at their breeding grounds. In fact, several migrants have advanced their spring arrival date, but whether such advancement has been sufficient to compensate for temporal shift in spring phenophases or, conversely, birds have become ecologically mismatched, is still an unanswered question, with very few exceptions. We used a novel approach based on accumulated winter and spring temperatures (degree-days) as a proxy for timing of spring biological events to test if the progress of spring at arrival to the breeding areas by 117 European migratory bird species has changed over the past five decades. Migrants, and particularly those wintering in sub-Saharan Africa, now arrive at higher degree-days and may have therefore accumulated a ‘thermal delay’, thus possibly becoming increasingly mismatched to spring phenology. Species with greater ‘thermal delay’ have shown larger population decline, and this evidence was not confounded by concomitant ecological factors or by phylogenetic effects. These findings provide general support to the largely untested hypotheses that migratory birds are becoming ecologically mismatched and that failure to respond to climate change can have severe negative impacts on their populations. The novel approach we adopted can be extended to the analysis of ecological consequences of phenological response to climate change by other taxa.     

Species with more volatile population dynamics are differentially impacted by weather

Climatic variation has been invoked as an explanation of population dynamics for a variety of taxa. Much work investigating the link between climatic forcings and population fluctuation uses single-taxon case studies. Here, we conduct comparative analyses of a multi-decadal dataset describing population dynamics of 50 co-occurring butterfly species at 10 sites in Northern California. Specifically, we explore the potential commonality of response to weather among species that encompass a gradient of population dynamics via a hierarchical Bayesian modelling framework. Results of this analysis demonstrate that certain weather conditions impact volatile, or irruptive, species differently as compared with relatively stable species. Notably, precipitation-related variables, including indices of the El Niño Southern Oscillation, have a more pronounced impact on the most volatile species. We hypothesize that these variables influence vegetation resource availability, and thus indirectly influence population dynamics of volatile taxa. As one of the first studies to show a common influence of weather among taxa with similar population dynamics, the results presented here suggest new lines of research in the field of biotic–abiotic interactions.     

Does flower phenology mirror the slowdown of global warming?

Although recent global warming trends in air temperature are not as pronounced as those observed only one decade ago, global mean temperature is still at a very high level. Does plant phenology – which is believed to be a suitable indicator of climate change – respond in a similar way, that is, does it still mirror recent temperature variations? We explored in detail long-term flowering onset dates of snowdrop, cherry, and lime tree and relevant spring temperatures at three sites in Germany (1901–2012) using the Bayesian multiple change-point approach. We investigated whether mean spring temperature changes were amplified or slowed down in the past decade and how plant phenology responded to the most recent temperature changes. Incorporating records with different end points (i.e., 2002 and 2012), we compared differences in trends and inferred possible differences caused by extrapolating phenological and meteorological data. The new multiple-change point approach is characterized by an enhanced structure and greater flexibility compared to the one change point model. However, the highest model probabilities for phenological (meteorological) records were still obtained for the one change point (linear) model. Marked warming trends in the recent decade were only revealed for mean temperatures of March to May, here better described with one or two change point models. In the majority of cases analyzed, changes in temperatures were well mirrored by phenological changes. However, temperatures in March to May were linked to less strongly advancing onset dates for lime tree flowering during the period 1901-2012, pointing to the likely influence of photoperiodic constraints or unfulfilled chilling requirements. Due to the slowdown of temperature increase, analyses conducted on records ending in 2002 demonstrated distinct differences when compared with records ending in 2012. Extrapolation of trends could therefore (along with the choice of the statistical method) lead to distinctly different results and most recent data should be integrated in order not to over- or underestimate future phenological changes.     

Epidemic malaria and warmer temperatures in recent decades in an East African highland

Climate change impacts on malaria are typically assessed with scenarios for the long-term future. Here we focus instead on the recent past (1970-2003) to address whether warmer temperatures have already increased the incidence of malaria in a highland region of East Africa. Our analyses rely on a new coupled mosquito-human model of malaria, which we use to compare projected disease levels with and without the observed temperature trend. Predicted malaria cases exhibit a highly nonlinear response to warming, with a significant increase from the 1970s to the 1990s, although typical epidemic sizes are below those observed. These findings suggest that climate change has already played an important role in the exacerbation of malaria in this region. As the observed changes in malaria are even larger than those predicted by our model, other factors previously suggested to explain all of the increase in malaria may be enhancing the impact of climate change.     

Disentangling effects of uncertainties on population projections: climate change impact on an epixylic bryophyte

Assessment of future ecosystem risks should account for the relevant uncertainty sources. This means accounting for the joint effects of climate variables and using modelling techniques that allow proper treatment of uncertainties. We investigate the influence of three of the IPCC's scenarios of greenhouse gas emissions (special report on emission scenarios (SRES)) on projections of the future abundance of a bryophyte model species. We also compare the relative importance of uncertainty sources on the population projections. The whole chain global climate model (GCM)-regional climate model-population dynamics model is addressed. The uncertainty depends on both natural- and model-related sources, in particular on GCM uncertainty. Ignoring the uncertainties gives an unwarranted impression of confidence in the results. The most likely population development of the bryophyte Buxbaumia viridis towards the end of this century is negative: even with a low-emission scenario, there is more than a 65 per cent risk for the population to be halved. The conclusion of a population decline is valid for all SRES scenarios investigated. Uncertainties are no longer an obstacle, but a mandatory aspect to include in the viability analysis of populations.     

缺失数据和贝叶斯系统发育分析精确度之探索

The effect of missing data on phylogenetic methods is a potentially important issue in our attempts to reconstruct the Tree of Life. If missing data are truly problematic, then it may be unwise to include species in an analysis that lack data for some characters (incomplete taxa) or to include characters that lack data for some species. Given the difficulty of obtaining data from all characters for all taxa (e.g., fossils), missing data might seriously impede efforts to reconstruct a comprehensive phylogeny that includes all species. Fortunately, recent simulations and empirical analyses suggest that missing data cells are not themselves problematic, and that incomplete taxa can be accurately placed as long as the overall number of characters in the analysis is large. However, these studies have so far only been conducted on parsimony, likelihood, and neighbor-joining methods. Although Bayesian phylogenetic methods have become widely used in recent years, the effects of missing data on Bayesian analysis have not been adequately studied. Here, we conduct simulations to test whether Bayesian analyses can accurately place incomplete taxa despite extensive missing data. In agreement with previous studies of other methods, we find that Bayesian analyses can accurately reconstruct the position of highly incomplete taxa (i.e., 95% missing data), as long as the overall number of characters in the analysis is large. These results suggest that highly incomplete taxa can be safely included in many Bayesian phylogenetic analyses.     

Avian population consequences of climate change are most severe for long-distance migrants in seasonal habitats

One consequence of climate change is an increasing mismatch between timing of food requirements and food availability. Such a mismatch is primarily expected in avian long-distance migrants because of their complex annual cycle, and in habitats with a seasonal food peak. Here we show that insectivorous long-distance migrant species in The Netherlands declined strongly (1984–2004) in forests, a habitat characterized by a short spring food peak, but that they did not decline in less seasonal marshes. Also, within generalist long-distance migrant species, populations declined more strongly in forests than in marshes. Forest-inhabiting migrant species arriving latest in spring declined most sharply, probably because their mismatch with the peak in food supply is greatest. Residents and short-distance migrants had non-declining populations in both habitats, suggesting that habitat quality did not deteriorate. Habitat-related differences in trends were most probably caused by climate change because at a European scale, long-distance migrants in forests declined more severely in western Europe, where springs have become considerably warmer, when compared with northern Europe, where temperatures during spring arrival and breeding have increased less. Our results suggest that trophic mismatches may have become a major cause for population declines in long-distance migrants in highly seasonal habitats.