Ecological theory predicts a positive and asymptotic relationship between plant diversity and ecosystem productivity based on the ability of more diverse plant communities to use limiting resources more fully. This is supported by recent empirical evidence. Additionally, in natural ecosystems, plant productivity is often a function of the presence and composition of mycorrhizal associations. Yet, the effect of mycorrhizal fungi on the relationship between plant diversity and productivity has not been investigated. We predict that in the presence of AMF, productivity will saturate at lower levels of species richness because AMF increase the ability of plant species to utilize nutrient resources. In this study we manipulated old‐field plant species richness in the presence and absence of two species of AMF. We found that in the absence of AMF, the relationship between plant species richness and productivity is positive and linear. However, in the presence of AMF, the relationship is positive but asymptotic, even though the maximum plant biomass was significantly different between the two AMF treatments. This is consistent with the hypothesis that AMF increase the redundancy of plant species in the productivity of plant communities, and indicates that these symbionts must be considered in future investigations of plant biodiversity and ecosystem function.

Growing concern about biodiversity loss underscores the need to quantify and understand temporal change. Here, we review the opportunities presented by biodiversity time series, and address three related issues: (i) recognizing the characteristics of temporal data; (ii) selecting appropriate statistical procedures for analysing temporal data; and (iii) inferring and forecasting biodiversity change. With regard to the first issue, we draw attention to defining characteristics of biodiversity time series—lack of physical boundaries, uni-dimensionality, autocorrelation and directionality—that inform the choice of analytic methods. Second, we explore methods of quantifying change in biodiversity at different timescales, noting that autocorrelation can be viewed as a feature that sheds light on the underlying structure of temporal change. Finally, we address the transition from inferring to forecasting biodiversity change, highlighting potential pitfalls associated with phase-shifts and novel conditions.

Plant communities have undergone dramatic changes in recent centuries, although not all such changes fit with the dominant biodiversity-crisis narrative used to describe them. At the global scale, future declines in plant species diversity are highly likely given habitat conversion in the tropics, although few extinctions have been documented for the Anthropocene to date (<0.1%). Nonnative species introductions have greatly increased plant species richness in many regions of the world at the same time that they have led to the creation of new hybrid polyploid species by bringing previously isolated congeners into close contact. At the local scale, conversion of primary vegetation to agriculture has decreased plant diversity, whereas other drivers of change-e.g., climate warming, habitat fragmentation, and nitrogen deposition-have highly context-dependent effects, resulting in a distribution of temporal trends with a mean close to zero. These results prompt a reassessment of how conservation goals are defined and justified.

Predicting the future ecological impact of global change drivers requires understanding how these same drivers have acted in the past to produce the plant populations and communities we see today. Historical ecological data sources have made contributions of central importance to global change biology, but remain outside the toolkit of most ecologists. Here we review the strengths and weaknesses of four unconventional sources of historical ecological data: land survey records, "legacy" vegetation data, historical maps and photographs, and herbarium specimens. We discuss recent contributions made using these data sources to understanding the impacts of habitat disturbance and climate change on plant populations and communities, and the duration of extinction-colonization time lags in response to landscape change. Historical data frequently support inferences made using conventional ecological studies (e.g., increases in warm-adapted species as temperature rises), but there are cases when the addition of different data sources leads to different conclusions (e.g., temporal vegetation change not as predicted by chronosequence studies). The explicit combination of historical and contemporary data sources is an especially powerful approach for unraveling long-term consequences of multiple drivers of global change. Despite the limitations of historical data, which include spotty and potentially biased spatial and temporal coverage, they often represent the only means of characterizing ecological phenomena in the past and have proven indispensable for characterizing the nature, magnitude, and generality of global change impacts on plant populations and communities.

Species distribution models (SDMs) are widely used in ecology. In theory, SDMs capture (at least part of ) species' ecological niches and can be used to make inferences about the distribution of suitable habitat for species of interest. Because habitat suitability is expected to influence population demography, SDMs have been used to estimate a variety of population parameters, from occurrence to genetic diversity. However, a critical look at the ability of SDMs to predict independent data across different aspects of population biology is lacking. Here, we systematically reviewed the literature, retrieving 201 studies that tested predictions from SDMs against independent assessments of occurrence, abundance, population performance, and genetic diversity. Although there is some support for the ability of SDMs to predict occurrence (~53% of studies depending on how support was assessed), the predictive performance of these models declines progressively from occurrence to abundance, to population mean fitness, to genetic diversity. At the same time, we observed higher success among studies that evaluated performance for single versus multiple species, pointing to a possible publication bias. Thus, the limited accuracy of SDMs reported here may reflect the best-case scenario. We discuss the limitations of these models and provide specific recommendations for their use for different applications going forward. However, we emphasize that predictions from SDMs, especially when used to inform conservation decisions, should be treated as hypotheses to be tested with independent data rather than as stand-ins for the population parameters we seek to know.