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Earth harbours an extraordinary plant phenotypic diversity 1 that is at risk from ongoing global changes 2,3 . However, it remains unknown how increasing aridity and livestock grazing pressure-two major drivers of global change 4-6 -shape the trait covariation that underlies plant phenotypic diversity 1,7 . Here we assessed how covariation among 20 chemical and morphological traits responds to aridity and grazing pressure within global drylands. Our analysis involved 133,769 trait measurements spanning 1,347 observations of 301 perennial plant species surveyed across 326 plots from 6 continents. Crossing an aridity threshold of approximately 0.7 (close to the transition between semi-arid and arid zones) led to an unexpected 88% increase in trait diversity. This threshold appeared in the presence of grazers, and moved toward lower aridity levels with increasing grazing pressure. Moreover, 57% of observed trait diversity occurred only in the most arid and grazed drylands, highlighting the phenotypic uniqueness of these extreme environments. Our work indicates that drylands act as a global reservoir of plant phenotypic diversity and challenge the pervasive view that harsh environmental conditions reduce plant trait diversity 8-10 . They also highlight that many alternative strategies may enable plants to cope with increases in environmental stress induced by climate change and land-use intensification., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Resilience is a major research focus covering a wide range of topics from biodiversity conservation to ecosystem (service) management. Model simulations can assess the resilience of, for example, plant species, measured as the return time to conditions prior to a disturbance. This requires process-based models (PBM) that implement relevant processes such as regeneration and reproduction and thus successfully reproduce transient dynamics after disturbances. Such models are often complex and thus limited to either short-term or small-scale applications, whereas many research questions require species predictions across larger spatial and temporal scales. We suggest a framework to couple a PBM and a statistical species distribution model (SDM), which transfers the results of a resilience analysis by the PBM to SDM predictions. The resulting hybrid model combines the advantages of both approaches: the convenient applicability of SDMs and the relevant process detail of PBMs in abrupt environmental change situations. First, we simulate dynamic responses of species communities to a disturbance event with a PBM. We aggregate the response behavior in two resilience metrics: return time and amplitude of the response peak. These metrics are then used to complement long-term SDM projections with dynamic short-term responses to disturbance. To illustrate our framework, we investigate the effect of abrupt short-term groundwater level and salinity changes on coastal vegetation at the German Baltic Sea. We found two example species to be largely resilient, and, consequently, modifications of SDM predictions consisted mostly of smoothing out peaks in the occurrence probability that were not confirmed by the PBM. Discrepancies between SDM- and PBM-predicted species responses were caused by community dynamics simulated in the PBM and absent from the SDM. Although demonstrated with boosted regression trees (SDM) and an existing individual-based model, IBC-grass (PBM), our flexible framework can easily be applied to other PBM and SDM types, as well as other definitions of short-term disturbances or long-term trends of environmental change. Thus, our framework allows accounting for biological feedbacks in the response to short- and long-term environmental changes as a major advancement in predictive vegetation modeling., (© 2018 by the Ecological Society of America.)

Current herbicide risk assessment guidelines for nontarget terrestrial plants require testing effects on young, vulnerable life stages (i.e., seedling emergence [and subsequent growth] and vegetative vigor [growth and dry wt]) but not directly on the reproduction of plants. However, the European Food Safety Authority (EFSA) has proposed that effects on reproduction might be considered when evaluating the potential effects on plants. We adapted the plant community model for grassland (IBC-grass) to give insight into the current debate on the sensitivity of reproductive versus vegetative endpoints in ecological risk assessment. In an extensive sensitivity analysis of this model, we compared plant attributes potentially affected by herbicides and the consequences for long-term plant population dynamics and plant diversity. This evaluation was implemented by reducing reproductive as well as vegetative endpoints by certain percentages (e.g., 10-90%) as a theoretical assumption. Plant mortality and seed sterility (i.e., inability of seeds to germinate) were the most sensitive attributes. Our results indicated that effects on seed production at off-field exposure rates must be very strong to have an impact on the risk assessment. Otherwise, effects on seed production are compensated for by the soil seed bank. The present study highlights the usefulness of community level modeling studies to support regulators in their decisions on the appropriate risk assessment endpoints and provides confidence in their assessments. Environ Toxicol Chem 2018;37:1707-1722. © 2018 SETAC., (© 2018 SETAC.)

Background: In Southern Africa savannas climate change has been proposed to alter rainfall, the most important environmental driver for woody plants. Woody plants are a major component of savanna vegetation determining rangeland condition and biodiversity. In this study we use a spatially explicit, stochastic computer model to assess the impact of climate change on the population dynamics of Grewia flava, a common, fleshy-fruited shrub species in the southern Kalahari. Understanding the population dynamics of Grewia flava is a crucial task, because it is widely involved in the shrub/bush encroachment process, a major concern for rangeland management due to its adverse effect on livestock carrying capacity and biodiversity., Results: For our study we consider four climate change scenarios that have been proposed for the southern Kalahari for the coming decades: (1) an increase in annual precipitation by 30-40%, (2) a decrease by 5-15%, (3) an increase in variation of extreme rainfall years by 10-20%, (4) and increase in temporal auto-correlation, i.e. increasing length and variation of periodic rainfall oscillations related to El Nino/La Nina phenomena. We evaluate the slope z of the time-shrub density relationship to quantify the population trend. For each climate change scenario we then compared the departure of z from typical stable population dynamics under current climatic conditions. Based on the simulation experiments we observed a positive population trend for scenario (1) and a negative trend for scenario (2). In terms of the projected rates of precipitation change for scenario (3) and (4) population dynamics were found to be relatively stable. However, for a larger increase in inter-annual variation or in temporal auto-correlation of rainfall population trends were negative, because favorable rainfall years had a limited positive impact due to the limited shrub carrying capacity., Conclusions: We conclude that a possible increase in precipitation will strongly facilitate shrub encroachment threatening savanna rangeland conditions and regional biodiversity. Furthermore, the negative effects found for positive auto-correlated rainfall support current ecological theory stating that periodically fluctuating environments can reduce population viability because species suffer disproportionately from poor environmental conditions.

There is concern about the lack of recruitment ofAcaciatrees in the Negev desert of Israel. We have developed three models to estimate the frequency of recruitment necessary for long-term population survival (i.e. positive average population growth for 1,000 years and<10% probability of extinction). Two models assume purely episodic recruitment based on the general notion that recruitment in arid environments is highly episodic. They differ in that the deterministic model investigates average dynamics while the stochastic model does not. Studies indicating that recruitment episodes in arid environments have been overemphasized motivated the development of the third model. This semi-stochastic model simulates a mixture of continuous and episodic recruitment. Model analysis was done analytically for the deterministic model and via running model simulations for the stochastic and semi-stochastic models. The deterministic and stochastic models predict that, on average, 2.2 and 3.7 recruitment events per century, respectively, are necessary to sustain the population. According to the semi-stochastic model, 1.6 large recruitment events per century and an annual probability of 50% that a small recruitment event occurs are needed. A consequence of purely episodic recruitment is that all recruitment episodes produce extremely large numbers of recruits (i.e. at odds with field observations), an evaluation that holds even when considering that rare events must be large. Thus, the semi-stochastic model appears to be the most realistic model. Comparing the prediction of the semi-stochastic model to field observations in the Negev desert shows that the absence of observations of extremely large recruitment events is no reason for concern. However, the almost complete absence of small recruitment events is a serious reason for concern. The lack of recruitment may be due to decreased densities of large mammalian herbivores and might be further exacerbated by possible changes in climate, both in terms of average precipitation and the temporal distribution of rain. [ABSTRACT FROM AUTHOR]