Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long‑term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modelling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002–2020 time series and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally-uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non‑photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote‑sensing‑based monitoring of trends in grasslands so that underlying processes can be discerned.

1. Of the several approaches that are used to analyze functional trait-environment relationships, the most popular is community-weighted mean regressions (CWMr) in which species trait values are averaged at the site level and then regressed against environmental variables. Other approaches include model-based methods and weighted correlations of different metrics of trait-environment associations, the best known of which is the fourth-corner correlation method. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using four different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios, implying that the significant results for the data could be spurious. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but had lower power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should always be avoided. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using five different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but suffered from low power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should be avoided.

Many researchers want to report an R2 to measure the variance explained by a model. When the model includes correlation among data, such as phylogenetic models and mixed models, defining an R2 faces two conceptual problems. (i) It is unclear how to measure the variance explained by predictor (independent) variables when the model contains covariances. (ii) Researchers may want the R2 to include the variance explained by the covariances by asking questions such as “How much of the data is explained by phylogeny?” Here, I investigate three R2s for phylogenetic and mixed models. R2resid is an extension of the ordinary least-squares R2 that weights residuals by variances and covariances estimated by the model; it is closely related to R2glmm presented by Nakagawa and Schielzeth (2013). R2pred is based on predicting each residual from the fitted model and computing the variance between observed and predicted values. R2lik is based on the likelihood of fitted models and therefore reflects the amount of information that the models contain. These three R2s are formulated as partial R2s, making it possible to compare the contributions of predictor variables and variance components (phylogenetic signal and random effects) to the fit of models. Because partial R2s compare a full model with a reduced model without components of the full model, they are distinct from marginal R2s that partition additive components of the variance. The properties of the R2s for phylogenetic models were assessed using simulations for continuous and binary response data (phylogenetic generalized least squares and phylogenetic logistic regression). Because the R2s are designed broadly for any model for correlated data, the R2s were also compared for LMMs and GLMMs. R2resid, R2pred, and R2lik all have similar performance in describing the variance explained by different components of models. However, R2pred gives the most direct answer to the question of how much variance in the data is explained by a model. R2resid is most appropriate for comparing models fit to different datasets, because it does not depend on sample sizes. And R2lik is most appropriate to assess the importance of different components within the same model applied to the same data, because it is most closely associated with statistical significance tests.

1. Species with unique phenologies have distinct trait syndromes and environmental affinities, yet there has been little exploration of whether community assembly processes differ for plants with different phenologies. In this study, we ask whether early- and late-blooming species differ in the ways that dispersal, persistence, and resource-acquisition traits shape plant occurrence patterns in patchy habitats. 2. We sampled plant communities in 51 Ozark dolomite glade grasslands, which range in size from <1 ha to >100 ha. We modelled the occurrence of 71 spring- and 43 summer-blooming grassland species these patches, using as predictors both environmental variables (landscape structure, soil resources) and plant traits related to dispersal, longevity, and resource acquisition. We were especially interested in how the environmental variables and plant traits interacted to determine the occurrence of phenological strategies in habitats that vary in size and isolation. 3. Summer-blooming species with better persistence and dispersal abilities had higher relative frequencies in smaller, more isolated habitat patches, and summer-blooming species with higher specific leaf area—suggesting fast growth and low stress tolerance—were more likely to occur in patches with greater soil organic matter and clay content. However, spring-blooming species showed much weaker interactions between functional traits and environmental gradients, perhaps because environmental conditions are less harsh in spring than in summer. 4. Synthesis: Several axes of plant life history variation may simultaneously influence community responses to habitat connectivity. In this case, explicitly considering plant phenology helped identify generalizable relationships between functional traits and landscape spatial structure.

Reduced-representation genome sequencing such as RADseq aids the analysis of genomes by reducing the quantity of data, thereby lowering both sequencing costs and computational burdens. RADseq was initially designed for studying genetic variation across genomes at the population level, but has also proved to be suitable for interspecific phylogeny reconstruction. RADseq data pose challenges for standard phylogenomic methods, however, due to incomplete coverage of the genome and large amounts of missing data. Alignment-free methods are both efficient and accurate for phylogenetic reconstructions with whole genomes and are especially practical for non-model organisms; nonetheless, alignment-free methods have not been applied with reduced genome sequencing data. Here, we test a full-genome assembly and alignment-free method, AAF, in application to RADseq data and propose two procedures for reads selection to remove reads from restriction sites that were not found in taxa being compared. We validate these methods using both simulations and real datasets. Reads selection improved the accuracy of phylogenetic construction in every simulated scenario and the two real datasets, making AAF as good or better than a comparable alignment-based method, even though AAF had much lower computational burdens. We also investigated the sources of missing data in RADseq and their effects on phylogeny reconstruction using AAF. The AAF pipeline modified for RADseq or other reduced-representation sequencing data, phyloRAD, is available on github (https://github.com/fanhuan/phyloRAD).

Interactions between multiple anthropogenic environmental changes can drive non-additive effects in ecological systems, and the non-additive effects can in turn be amplified or dampened by spatial covariation among environmental changes. We investigated the combined effects of night-time warming and light pollution on pea aphids and two predatory ladybeetle species. As expected, neither night-time warming nor light pollution changed the suppression of aphids by the ladybeetle species that forages effectively in darkness. However, for the more-visual predator, warming and light had non-additive effects in which together they caused much lower aphid abundances. These results are particularly relevant for agriculture near urban areas that experience both light pollution and warming from urban heat islands. Because warming and light pollution can have non-additive effects, predicting their possible combined consequences over broad spatial scales requires knowing how they co-occur. We found that night-time temperature change since 1949 covaried positively with light pollution, which has the potential to increase their non-additive effects on pea aphid control by 70% in US alfalfa. Our results highlight the importance of non-additive effects of multiple environmental factors on species and food webs, especially when these factors co-occur.

Long-term environmental changes will likely alter the strengths of interactions between species and consequently their population dynamics, leading to changes in the stability of ecological systems. While an increasing number of empirical studies have shown that environmental changes can alter the strengths of species interactions, these studies are typically short (<1–2 generations) and therefore give only partial information about longer term population dynamics. To focus on longer term dynamics, we investigated population cycles of pea aphids and their most common parasitoid, Aphidius ervi, in Wisconsin, USA. Data collected over three years in alfalfa fields showed an apparent host–parasitoid population cycle. Furthermore, higher pea aphid population growth rates and increased parasitism were correlated with higher naturally occurring temperatures. While these effects were observed with seasonal fluctuations in temperature, they beg the question of how long-term changes in mean annual temperature might change aphid–parasitoid population cycles, a question which we further pursued with laboratory experiments. To quantify temperature-dependent demographic parameters, we used short-term (<1 generation) experiments conducted at 20°C and 27°C. The higher temperature increased aphid and parasitoid development rates, adult aphid life span and fecundity, and parasitoid attack rates. We then conducted multi-generation population-level laboratory experiments to reveal the effects of temperature (20°C vs. 27°C) on population dynamics. We fit the resulting time series data using a nonlinear age-structured state-space model to estimate population-level processes that could not be estimated in short-term laboratory experiments. Using the model, we parsed out the demographic rates that had the largest impacts on aphid–parasitoid population cycles. This analysis showed that there were frequent contrasts in the effects of temperature operating through different demographic rates. For example, the temperature-dependent increase in aphid development rate decreased cycle amplitude, while the increase in parasitoid attack rate increased cycle amplitude. There were also striking interactions among demographic rates. For example, the temperature-dependent increase in aphid development rate could either increase or decrease the cycle period depending on the values of other demographic rates. Although these complexities make predictions difficult, overall they suggest that increasing long-term mean temperature will decrease the period, increase the amplitude, and tend to destabilize pea aphid–A. ervi dynamics.