The ecological importance of the common hippopotamus (Hippopotamus amphibius) in aquatic ecosystems is becoming increasingly well known. These unique megaherbivores are also likely to have a formative influence on the terrestrial ecosystems in which they forage. In this study, we employed a novel exclosure design to exclude H. amphibius from experimental plots on near-river grasslands. Our three-year implementation of this experiment revealed a substantial influence of H. amphibius removal on both plant communities and soil chemistry. H. amphibius significantly reduced grassland canopy height, increased the leafiness of common grasses, reduced woody plant abundance and size, and increased the concentrations of several soil elements. Many of the soil chemistry changes that we experimentally induced by exclusion of H. amphibius were mirrored in the soil chemistry differences between naturally occurring habitats of frequent (grazing lawns) and infrequent (shrub forest) use by H. amphibius and other grazing herbivores. In contrast to existing hypotheses regarding grazing species, we found that H. amphibius had little effect on local plant species richness. Simultaneous observations of exclosures designed to remove all large herbivores revealed that H. amphibius removal had ecologically significant impacts, but that the removal of all species of large herbivores generated more pronounced impacts than the removal of H. amphibius alone. In aggregate, our results suggest that H. amphibius have myriad effects on their terrestrial habitats that likely improve the quality of forage available for other herbivores. We suggest that ongoing losses of this vulnerable megaherbivore are likely to cause significant ecological change.

In order to understand how the effects of land‐use change vary among taxa and environmental contexts, we investigate how three types of land‐use change have influenced phylogenetic diversity (PD) and species composition of three functionally distinct communities: plants, small mammals, and large mammals. We found large mammal communities were by far the most heavily impacted by land‐use change, with areas of attempted large wildlife exclusion and intense livestock grazing, respectively, containing 164 and 165 million fewer years of evolutionary history than conserved areas (~40% declines). The effects of land‐use change on PD varied substantially across taxa, type of land‐use change, and, for most groups, also across abiotic conditions. This highlights the need for taxa‐specific or multi‐taxa evaluations, for managers interested in conserving specific groups or whole communities, respectively. It also suggests that efforts to conserve and restore PD may be most successful if they focus on areas of particular land‐use types and abiotic conditions. Importantly, we also describe the substantial species turnover and compositional changes that cannot be detected by alpha diversity metrics, emphasizing that neither PD nor other taxonomic diversity metrics are sufficient proxies for ecological integrity. Finally, our results provide further support for the emerging consensus that conserved landscapes are critical to support intact assemblages of some lineages such as large mammals, but that mosaics of disturbed land‐uses, including both agricultural and pastoral land, do provide important habitats for a diverse array of plants and small mammals.

Are CTS patients receiving equal standards of care across the country?

Pollinator foraging behavior has direct consequences for plant reproduction and has been implicated in driving floral trait evolution. Exploring the degree to which pollinators exhibit flexibility in foraging behavior will add to a mechanistic understanding of how pollinators can impose selection on plant traits. Although plants have evolved suites of floral traits to attract pollinators, flower color is a particularly important aspect of the floral display. Some pollinators show strong innate color preference, but many pollinators display flexibility in preference due to learning associations between rewards and color, or due to variable perception of color in different environments or plant communities. This study examines the flexibility in flower color preference of two groups of native butterfly pollinators under natural field conditions. We find that pipevine swallowtails (Battus philenor) and skippers (family Hesperiidae), the predominate pollinators of the two native Texas Phlox species, Phlox cuspidata and Phlox drummondii, display distinct patterns of color preferences across different contexts. Pipevine swallowtails exhibit highly flexible color preferences and likely utilize other floral traits to make foraging decisions. In contrast, skippers have consistent color preferences and likely use flower color as a primary cue for foraging. As a result of this variation in color preference flexibility, the two pollinator groups impose concordant selection on flower color in some contexts but discordant selection in other contexts. This variability could have profound implications for how flower traits respond to pollinator‐mediated selection. Our findings suggest that studying dynamics of behavior in natural field conditions is important for understanding plant–pollinator interactions.

Neighborhood models have allowed us to test many hypotheses regarding the drivers of variation in tree growth, but require considerable computation due to the many empirically supported non-linear relationships they include. Regularized regression represents a far more efficient neighborhood modeling method, but it is unclear whether such an ecologically unrealistic model can provide accurate insights on tree growth. Rapid computation is becoming increasingly important as ecological datasets grow in size, and may be essential when using neighborhood models to predict tree growth beyond sample plots or into the future. We built a novel regularized regression model of tree growth and investigated whether it reached the same conclusions as a commonly used neighborhood model, regarding hypotheses of how tree growth is influenced by the species identity of neighboring trees. We also evaluated the ability of both models to interpolate the growth of trees not included in the model fitting dataset. Our regularized regression model replicated most of the classical model’s inferences in a fraction of the time without using high-performance computing resources. We found that both methods could interpolate out-of-sample tree growth, but the method making the most accurate predictions varied among focal species. Regularized regression is particularly efficient for comparing hypotheses because it automates the process of model selection and can handle correlated explanatory variables. This feature means that regularized regression could also be used to select among potential explanatory variables (e.g., climate variables) and thereby streamline the development of a classical neighborhood model. Both regularized regression and classical methods can interpolate out-of-sample tree growth, but future research must determine whether predictions can be extrapolated to trees experiencing novel conditions. Overall, we conclude that regularized regression methods can complement classical methods in the investigation of tree growth drivers and represent a valuable tool for advancing this field toward prediction.