The Neolithic Plant Invasion Hypothesis: the role of preadaptation and disturbance in grassland invasion.

A long-standing hypothesis is that many European plants invade temperate grasslands globally because they are introduced simultaneously with pastoralism and cultivation, to which they are 'preadapted' after millennia of exposure dating to the Neolithic era ('Neolithic Plant Invasion Hypothesis' (NPIH)). These 'preadaptations' are predicted to maximize their performance relative to native species lacking this adaptive history. Here, we discuss the explanatory relevance of the NPIH, clarifying the importance of evolutionary context vs other mechanisms driving invasion. The NPIH makes intuitive sense given established connections between invasion and agricultural-based perturbation. However, tests are often incomplete given the need for performance contrasts between home and away ranges, while controlling for other mechanisms. We emphasize six NPIH-based predictions, centring on trait similarity of invaders between home vs away populations, and differing perturbation responses by invading and native plants. Although no research has integrated all six predictions, we highlight studies suggesting preadaptation influences on invasion. Given that many European grasslands are creations of human activity from the past, current invasions by these flora may represent the continuation of processes dating to the Neolithic. Ironically, European Neolithic-derived grasslands are becoming rarer, reflecting changes in management and illustrating the importance of human influences on these species.

Using plant traits to predict the sensitivity of colonizations and extirpations to landscape context.

Theory suggests that species with different traits will respond differently to landscape fragmentation. Studies have shown that the presence of species in fragments of varying size, shape and connectivity is dependent on plant traits related to dispersal ability, persistence and disturbance tolerance. However, the role of traits in determining long-term plant community changes in response to changing landscape context is not well understood. We used data from resurveys of 184 plots to test the ability of nine plant traits to predict colonizations and extirpations between 1968 and 2009 based on the surrounding landscape context. We related apparent colonizations and extirpations to road density, naturally vegetated area and patch shape and then tested for significant relationships between a tendency for positive or negative associations and plant traits. Exotic, herbaceous, annual, shade-intolerant species and species with higher specific leaf area were more likely than others to colonize plots with higher road density, lower amount of naturally vegetated area and higher edge-to-area ratio. However, extirpations were rarely predictable based on traits. The role of landscape context in structuring plant community change over the past four decades in the 184 plots resurveyed was largely mediated by colonization events, suggesting that trait-based extirpations occur with a longer post-fragmentation time lag or, alternatively, that extirpation is more stochastic with respect to plant traits than is colonization.

Quantifying temporal change in biodiversity: challenges and opportunities.

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.

Historical ecology: using unconventional data sources to test for effects of global environmental change.

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.

Do traits of plant species predict the efficacy of species distribution models for finding new occurrences?

Species distribution models (SDMs) are used to test ecological theory and to direct targeted surveys for species of conservation concern. Several studies have tested for an influence of species traits on the predictive accuracy of SDMs. However, most used the same set of environmental predictors for all species and/or did not use truly independent data to test SDM accuracy. We built eight SDMs for each of 24 plant species of conservation concern, varying the environmental predictors included in each SDM version. We then measured the accuracy of each SDM using independent presence and absence data to calculate area under the receiver operating characteristic curve (AUC) and true positive rate (TPR). We used generalized linear mixed models to test for a relationship between species traits and SDM accuracy, while accounting for variation in SDM performance that might be introduced by different predictor sets. All traits affected one or both SDM accuracy measures. Species with lighter seeds, animal-dispersed seeds, and a higher density of occurrences had higher AUC and TPR than other species, all else being equal. Long-lived woody species had higher AUC than herbaceous species, but lower TPR. These results support the hypothesis that the strength of species-environment correlations is affected by characteristics of species or their geographic distributions. However, because each species has multiple traits, and because AUC and TPR can be affected differently, there is no straightforward way to determine a priori which species will yield useful SDMs based on their traits. Most species yielded at least one useful SDM. Therefore, it is worthwhile to build and test SDMs for the purpose of finding new populations of plant species of conservation concern, regardless of these species' traits.

Context-dependent interactions and the regulation of species richness in freshwater fish.

Species richness is regulated by a complex network of scale-dependent processes. This complexity can obscure the influence of limiting species interactions, making it difficult to determine if abiotic or biotic drivers are more predominant regulators of richness. Using integrative modeling of freshwater fish richness from 721 lakes along an 11o latitudinal gradient, we find negative interactions to be a relatively minor independent predictor of species richness in lakes despite the widespread presence of predators. Instead, interaction effects, when detectable among major functional groups and 231 species pairs, were strong, often positive, but contextually dependent on environment. These results are consistent with the idea that negative interactions internally structure lake communities but do not consistently 'scale-up' to regulate richness independently of the environment. The importance of environment for interaction outcomes and its role in the regulation of species richness highlights the potential sensitivity of fish communities to the environmental changes affecting lakes globally.

Plant Biodiversity Change Across Scales During the Anthropocene.

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.