Climate change has the potential to affect the ecology and evolution of every species on Earth. Although the ecological consequences of climate change are increasingly well documented, the effects of climate on the key evolutionary process driving adaptationnatural selection-are largely unknown. We report that aspects of precipitation and potential evapotranspiration, along with the North Atlantic Oscillation, predicted variation in selection across plant and animal populations throughout many terrestrial biomes, whereas temperature explained little variation. By showing that selection was influenced by climate variation, our results indicate that climate change may cause widespread alterations in selection regimes, potentially shifting evolutionary trajectories at a global scale.C limate affects organisms in ways that ultimately shape patterns of biodiversity (1). Consequently, the rapid changes in Earth's recent climate impose challenges for many organisms, often reducing population fitness (2-4). Although some species may migrate and undergo range shifts to avoid climate-induced declines and potential extinction (5), an alternative outcome is adaptive evolution in response to selection imposed by climate (6). However, we lack a general understanding of whether local and global climatic factors such as temperature, precipitation, and water availability influence selection (2, 7). Understanding these effects is critical for predicting the consequences of increasing droughts, heat waves, and extreme precipitation events that are expected in many regions (8, 9).To quantify how climate variation influences selection, we assembled a large database of standardized directional selection gradients and differentials from spatially [mean = 4.6 ± 5.4 (SD) populations, range = 2 to 59 populations] and temporally [mean = 5.2 ± 6.8 (SD) years, range = 2 to 45 years] replicated selection studies (N = 168) in plant and animal populations (Table 1 and database S1). We focused on directional selection that can generate increases or decreases in trait values because it is well characterized and is likely to drive rapid evolution (10) in response to variation in climatic factors. However, selection acting on trait combinations and trait variance may also be affected by climate (7). Selection gradients estimate the strength and direction of selection acting directly on a trait, whereas differentials estimate "total selection" on a trait via both direct and indirect selection because of trait correlations (11). These standardized selection coefficients describe selection in terms of the relationship between relative fitness and quantitative traits measured in standard deviations, thus facilitating cross-study comparisons (11,12).Geographically, the database contains many estimates of selection from temperate, mid-latitude regions centered at 40°N (Fig. 1A). The populations in this database span many terrestrial biomes on Earth, with the exception of tundra and tropical rainforests where selection has rarely been quantified (Fig. 1B...

Recent research has highlighted interdependencies between dispersal and other life-history traits, i.e. dispersal syndromes, thereby revealing constraints on the evolution of dispersal and opportunities for improved ability to predict dispersal by considering suites of dispersal-related traits. This review adds to the growing list of life-history traits linked to spatial dispersal by emphasising the interdependence between dispersal through space and time, i.e. life-history diversity that distributes individuals into separate reproductive events. We reviewed the literature that has simultaneously investigated spatial and temporal dispersal to examine the prediction that traits of these two dispersal strategies are negatively correlated. Our results suggest that negative covariation is widely anticipated from theory. Empirical studies often reported evidence of weak negative covariation, although more complicated patterns were also evident, including across levels of biological organisation. Existing literature has largely focused on plants with dormancy capability, one or two phases of the dispersal process (emigration and/or transfer) and a single level of biological organisation (theory: individual; empirical: species). We highlight patterns of covariation across levels of organisation and conclude with a discussion of the consequences of dispersal through space and time and future research areas that should improve our understanding of dispersalrelated life-history syndromes.

Abstract. Whether different sources of mortality are additive, compensatory, or depensatory is a key question in population biology. A way to test for additivity is to calculate the correlation between cause-specific mortality rates obtained from marked animals. However, existing methods to estimate this correlation raise several methodological issues. One difficulty is the existence of an intrinsic bias in the correlation parameter. Although this bias can be formally expressed, it requires knowledge about natural survival without any competing mortality source, which is difficult to assess in most cases. Another difficulty lies in estimating the true process correlation while properly accounting for sampling variation. Using a Bayesian approach, we developed a state-space model to assess the correlation between two competing sources of mortality. By distinguishing the mortality process from its observation through dead recoveries and live recaptures, we estimated the process correlation. To correct for the intrinsic bias, we incorporated experts' opinions on natural survival. We illustrated our approach using data on a hunted population of wild boars. Mortalities were not additive and natural mortality increased with hunting mortality likely as a consequence of non-controlled mortality by crippling loss. Our method opens perspectives for wildlife management and for the conservation of endangered species.

The life history schedules of wild organisms have long attracted scientific interest, and, in light of ongoing climate change, an understanding of their genetic and environmental underpinnings is increasingly becoming of applied concern. We used a multi‐generation pedigree and detailed phenotypic records, spanning 18 years, to estimate the quantitative genetic influences on the timing of hibernation emergence in a wild population of Columbian ground squirrels (Urocitellus columbianus). Emergence date was significantly heritable [h2 = 0.22 ± 0.05 (in females) and 0.34 ± 0.14 (in males)], and there was a positive genetic correlation (rG = 0.76 ± 0.22) between male and female emergence dates. In adult females, the heritabilities of body mass at emergence and oestrous date were h2 = 0.23 ± 0.09 and h2 = 0.18 ± 0.12, respectively. The date of hibernation emergence has been hypothesized to have evolved so as to synchronize subsequent reproduction with upcoming peaks in vegetation abundance. In support of this hypothesis, although levels of phenotypic variance in emergence date were higher than oestrous date, there was a highly significant genetic correlation between the two (rG = 0.98 ± 0.01). Hibernation is a prominent feature in the annual cycle of many small mammals, but our understanding of its influences lags behind that for phenological traits in many other taxa. Our results provide the first insight into its quantitative genetic influences and thus help contribute to a more general understanding of its evolutionary significance.