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Global climate change has triggered an urgent need for predicting the reorganization of Earth’s biodiversity. For dioecious species (those with separate sexes), it is unclear how commonly unique climate sensitivities of females and males could influence projections for species-level responses to climate change. We developed demographic models of range limitation, parameterized from geographically distributed common garden experiments, with females and males of a dioecious grass species (Poa arachnifera) throughout and beyond its range in the south-central U.S. We contrasted predictions of a standard female-dominant model with those of a two-sex model that accounts for feedbacks between sex ratio and vital rates. Both model versions predict that future climate change will induce a poleward shift of niche suitability beyond current northern limits. However, the magnitude of the poleward shift was underestimated by the female-dominant model because females have broader temperature tolerance than males but become mate-limited under female-biased sex ratios, which are forecasted to become more common under future climate. Our results illustrate how explicitly accounting for both sexes can enhance population viability forecasts and conservation planning for dioecious species in response to climate change. 

Global loss of biodiversity and its associated ecosystem services is occurring at an alarming rate and is predicted to accelerate in the future. Metacommunity theory provides a framework to investigate multi-scale processes that drive change in biodiversity across space and time. Short-term ecological studies across space have progressed our understanding of biodiversity through a metacommunity lens, however, such snapshots in time have been limited in their ability to explain which processes, at which scales, generate observed spatial patterns. Temporal dynamics of metacommunities have been understudied, and large gaps in theory and empirical data have hindered progress in our understanding of underlying metacommunity processes that give rise to biodiversity patterns. Fortunately, we are at an important point in the history of ecology, where long-term studies with cross-scale spatial replication provide a means to gain a deeper understanding of the multiscale processes driving biodiversity patterns in time and space to inform metacommunity theory. The maturation of coordinated research and observation networks, such as the United States Long Term Ecological Research (LTER) program, provides an opportunity to advance explanation and prediction of biodiversity change with observational and experimental data at spatial and temporal scales greater than any single research group could accomplish. Synthesis of LTER network community datasets illustrates that long-term studies with spatial replication present an under-utilized resource for advancing spatio-temporal metacommunity research. We identify challenges towards synthesizing these data and present recommendations for addressing these challenges. We conclude with insights about how future monitoring efforts by coordinated research and observation networks could further the development of metacommunity theory and its applications aimed at improving conservation efforts. 

Abstract There is an urgent need to synthesize the state of our knowledge on plant responses to climate. The availability of open-access data provide opportunities to examine quantitative generalizations regarding which biomes and species are most responsive to climate drivers. Here, we synthesize time series of structured population models from 162 populations of 62 plants, mostly herbaceous species from temperate biomes, to link plant population growth rates (λ) to precipitation and temperature drivers. We expect: (1) more pronounced demographic responses to precipitation than temperature, especially in arid biomes; and (2) a higher climate sensitivity in short-lived rather than long-lived species. We find that precipitation anomalies have a nearly three-fold larger effect onλthan temperature. Species with shorter generation time have much stronger absolute responses to climate anomalies. We conclude that key species-level traits can predict plant population responses to climate, and discuss the relevance of this generalization for conservation planning. 

Abstract The relationship between biodiversity and stability, or its inverse, temporal variability, is multidimensional and complex. Temporal variability in aggregate properties, like total biomass or abundance, is typically lower in communities with higher species diversity (i.e., the diversity–stability relationship [DSR]). At broader spatial extents, regional‐scale aggregate variability is also lower with higher regional diversity (in plant systems) and with lower spatial synchrony. However, focusing exclusively on aggregate properties of communities may overlook potentially destabilizing compositional shifts. It is not yet clear how diversity is related to different components of variability across spatial scales, nor whether regional DSRs emerge across a broad range of organisms and ecosystem types. To test these questions, we compiled a large collection of long‐term metacommunity data spanning a wide range of taxonomic groups (e.g., birds, fish, plants, invertebrates) and ecosystem types (e.g., deserts, forests, oceans). We applied a newly developed quantitative framework for jointly analyzing aggregate and compositional variability across scales. We quantified DSRs for composition and aggregate variability in local communities and metacommunities. At the local scale, more diverse communities were less variable, but this effect was stronger for aggregate than compositional properties. We found no stabilizing effect of γ‐diversity on metacommunity variability, but β‐diversity played a strong role in reducing compositional spatial synchrony, which reduced regional variability. Spatial synchrony differed among taxa, suggesting differences in stabilization by spatial processes. However, metacommunity variability was more strongly driven by local variability than by spatial synchrony. Across a broader range of taxa, our results suggest that high γ‐diversity does not consistently stabilize aggregate properties at regional scales without sufficient spatial β‐diversity to reduce spatial synchrony. 

Abstract Understanding the effects of climate on the vital rates (e.g., survival, development, reproduction) and dynamics of natural populations is a long‐standing quest in ecology, with ever‐increasing relevance in the face of climate change. However, linking climate drivers to demographic processes requires identifying the appropriate time windows during which climate influences vital rates. Researchers often do not have access to the long‐term data required to test a large number of windows, and are thus forced to makea priorichoices. In this study, we first synthesize the literature to assess currenta priorichoices employed in studies performed on 104 plant species that link climate drivers with demographic responses. Second, we use a sliding‐window approach to investigate which combination of climate drivers and temporal window have the best predictive ability for vital rates of four perennial plant species that each have over a decade of demographic data (Helianthella quinquenervis,Frasera speciosa,Cylindriopuntia imbricata, andCryptantha flava). Our literature review shows that most studies consider time windows in only the year preceding the measurement of the vital rate(s) of interest, and focus on annual or growing season temporal scales. In contrast, our sliding‐window analysis shows that in only four out of 13 vital rates the selected climate drivers have time windows that align with, or are similar to, the growing season. For many vital rates, the best window lagged more than 1 year and up to 4 years before the measurement of the vital rate. Our results demonstrate that for the vital rates of these four species, climate drivers that are lagged or outside of the growing season are the norm. Our study suggests that considering climatic predictors that fall outside of the most recent growing season will improve our understanding of how climate affects population dynamics. 

Abstract Population dynamics play a central role in the historical and current development of fundamental and applied ecological science. The nascent culture of open data promises to increase the value of population dynamics studies to the field of ecology. However, synthesis of population data is constrained by the difficulty in identifying relevant datasets, by the heterogeneity of available data and by access to raw (as opposed to aggregated or derived) observations.To obviate these issues, we built a relational database,popler, and itsRclient, the library popler.popleraccommodates the vast majority of population data under a common structure, and without the need for aggregating raw observations. The popler R library is designed for users unfamiliar with the structure of the database and with the SQL language. ThisRlibrary allows users to identify, download, explore and cite datasets salient to their needs.We implemented popler as a PostgreSQL instance, where we stored population data originated by the United States Long Term Ecological Research (LTER) Network. Our focus on the US LTER data aims to leverage the potential of this vast open data resource. The database currently contains 305 datasets from 25 LTER sites.popleris designed to accommodate automatic updates of existing datasets, and to accommodate additional datasets from LTER as well as non‐LTER studies.The combination of the online database and theRlibrary popler is a resource for data synthesis efforts in population ecology. The common structure ofpoplersimplifies comparative analyses, and the availability of raw data confers flexibility in data analysis. The popler R library maximizes these opportunities by providing a user‐friendly interface to the online database.