Deriving Population Scaling Rules from Individual-Level Metabolism and Life History Traits.

AbstractIndividual metabolism generally scales with body mass with an exponent around 3/4. From dimensional arguments it follows that maximum population growth rate (rmax) scales with a -1/4 exponent. However, the dimensional argument implicitly assumes that offspring size is proportional to adult size. Here, we calculate rmax from metabolic scaling at the level of individuals within size-structured populations while explicitly accounting for offspring size. We identify four general patterns of how rmax scales with adult mass based on four empirical life history patterns employed by groups of species. These life history patterns are determined by how traits of somatic growth rate and/or offspring mass relate to adult mass. One life history pattern-constant adult-to-offspring mass ratio and somatic growth rate independent of adult mass-leads to the classic -1/4 scaling of rmax. The other three life history patterns either lead to nonmetabolic population growth scaling with adult mass or do not follow a power-law relationship at all. Using life history data on five marine taxa and terrestrial mammals, we identify species groups that belong to one of each case. We predict that elasmobranchs, copepods, and mammals follow standard -1/4 power-law scaling, whereas teleost fish and bivalves do not have a pure power-law scaling. Our work highlights how taxa may deviate from the classic -1/4 metabolic scaling pattern of maximum population growth. The approach is generic and can be applied to any taxa.

The within-population variability of leaf spring and autumn phenology is influenced by temperature in temperate deciduous trees.

Leaf phenology is a major driver of ecosystem functioning in temperate forests and a robust indicator of climate change. Both the inter-annual and inter-population variability of leaf phenology have received much attention in the literature; in contrast, the within-population variability of leaf phenology has been far less studied. Beyond its impact on individual tree physiological processes, the within-population variability of leaf phenology can affect the estimation of the average budburst or leaf senescence dates at the population scale. Here, we monitored the progress of spring and autumn leaf phenology over 14 tree populations (9 tree species) in six European forests over the period of 2011 to 2018 (yielding 16 site-years of data for spring, 14 for autumn). We monitored 27 to 512 (with a median of 62) individuals per population. We quantified the within-population variability of leaf phenology as the standard deviation of the distribution of individual dates of budburst or leaf senescence (SDBBi and SDLSi, respectively). Given the natural variability of phenological dates occurring in our tree populations, we estimated from the data that a minimum sample size of 28 (resp. 23) individuals, are required to estimate SDBBi (resp. SDLSi) with a precision of 3 (resp. 7) days. The within-population of leaf senescence (average SDLSi = 8.5 days) was on average two times larger than for budburst (average SDBBi = 4.0 days). We evidenced that warmer temperature during the budburst period and a late average budburst date were associated with a lower SDBBi, as a result of a quicker spread of budburst in tree populations, with a strong species effect. Regarding autumn phenology, we observed that later senescence and warm temperatures during the senescence period were linked with a high SDLSi, with a strong species effect. The shares of variance explained by our models were modest suggesting that other factors likely influence the within-population variation in leaf phenology. For instance, a detailed analysis revealed that summer temperatures were negatively correlated with a lower SDLSi.