We integrate climatic niche models and dated phylogenies to characterize the evolution of climatic niches in Oenothera sections Anogra and Kleinia (Onagraceae), and from that we make inferences on diversification in relation to climate. The evolution of climatic tolerances in Anogra + Kleinia has been heterogeneous, across phylogenetic groups and across different dimensions of climate. All the extant taxa occur in semiarid to arid conditions (annual precipitation of 10.1-49.1 cm and high temperatures in the warmest month of 28.5 degrees-40.1 degrees C), but there is striking variation among taxa in their climatic tolerances, especially temperature (minimum temperatures in the coldest month of -14.0 degrees to 5.3 degrees C) and summer versus winter precipitation (precipitation in the warmest quarter of 0.6-19.4 cm). Climatic disparity is especially pronounced in two subclades (californica, deltoides) that radiated in the southwestern United States and California, apparently including both divergent and convergent evolution of climatic tolerances. This niche evolution is remarkable, given the probable timescale of the radiation (approximately 1 million years). We suggest that the spatiotemporal climatic heterogeneity of western North America has served as a driver of diversification. Our data are also consistent with Axelrod's hypothesis that the spread of arid conditions in western North America stimulated diversification of arid-adapted lineages.

Summary1. Integral projection models (IPMs) use information on how an individual's state influences its vital rates -survival, growth and reproduction -to make population projections. IPMs are constructed from regression models predicting vital rates from state variables (e.g. size or age) and covariates (e.g. environment). By combining regressions of vital rates, an IPM provides mechanistic insight into emergent ecological patterns such as population dynamics, species geographic distributions or life-history strategies. 2. Here, we review important resources for building IPMs and provide a comprehensive guide, with extensive R code, for their construction. IPMs can be applied to any stage-structured population; here, we illustrate IPMs for a series of plant life histories of increasing complexity and biological realism, highlighting the utility of various regression methods for capturing biological patterns. We also present case studies illustrating how IPMs can be used to predict species' geographic distributions and life-history strategies. 3. IPMs can represent a wide range of life histories at any desired level of biological detail. Much of the strength of IPMs lies in the strength of regression models. Many subtleties arise when scaling from vital rate regressions to population-level patterns, so we provide a set of diagnostics and guidelines to ensure that models are biologically plausible. Moreover, IPMs can exploit a large existing suite of analytical tools developed for matrix projection models.

Many species produce eggs or seeds that refrain from hatching despite developmental preparedness and favorable environmental conditions. Instead, these propagules hatch in intervals over long periods. Such variable hatch or germination tactics may represent bet-hedging against future catastrophes. Empiricists have independently recognized these approaches in diverse species. Terms such as seed banking, delayed egg hatching, and embryonic diapause have been used to describe these tactics, but connections between fields of study have been rare. Here we suggest a general term, germ banking, to incorporate all previous terms, unifying many seemingly disparate biological strategies under a single definition. We define the phenomenon of germ banking and use several biological examples to illustrate it. We then discuss the different causes of variation in emergence timing, delineate which constitute germ banking, and distinguish between germ banking and optimal timing of diapause. The wide-ranging consequences of germ banking are discussed, including modification of the age structure of a population, the alteration of microevolutionary dynamics, the migration of alleles from the past, the maintenance of genetic and species diversity, and the promotion of species coexistence. We end by posing questions to direct future research.

Predicting long-term trends in forest growth requires accurate characterisation of how the relationship between forest productivity and climatic stress varies across climatic regimes. Using a network of over two million tree-ring observations spanning North America and a space-for-time substitution methodology, we forecast climate impacts on future forest growth. We explored differing scenarios of increased water-use efficiency (WUE) due to CO2 -fertilisation, which we simulated as increased effective precipitation. In our forecasts: (1) climate change negatively impacted forest growth rates in the interior west and positively impacted forest growth along the western, southeastern and northeastern coasts; (2) shifting climate sensitivities offset positive effects of warming on high-latitude forests, leaving no evidence for continued 'boreal greening'; and (3) it took a 72% WUE enhancement to compensate for continentally averaged growth declines under RCP 8.5. Our results highlight the importance of locally adapted forest management strategies to handle regional differences in growth responses to climate change.

Understanding and forecasting species' geographic distributions in the face of global change is a central priority in biodiversity science. The existing view is that one must choose between correlative models for many species versus process-based models for few species. We suggest that opportunities exist to produce process-based range models for many species, by using hierarchical and inverse modeling to borrow strength across species, fill data gaps, fuse diverse data sets, and model across biological and spatial scales. We review the statistical ecology and population and range modeling literature, illustrating these modeling strategies in action. A variety of large, coordinated ecological datasets that can feed into these modeling solutions already exist, and we highlight organisms that seem ripe for the challenge.