Science for a wilder Anthropocene: synthesis and future directions for trophic rewilding research

Trophic rewilding is an ecological restoration strategy that uses species introductions to restore top-down trophic interactions and associated trophic cascades to promote self-regulating biodiverse ecosystems. Given the importance of large animals in trophic cascades and their widespread losses and resulting trophic downgrading, it often focuses on restoring functional megafaunas. Trophic rewilding is increasingly being implemented for conservation, but remains controversial. Here, we provide a synthesis of its current scientific basis, highlighting trophic cascades as the key conceptual framework, discussing the main lessons learned from ongoing rewilding projects, systematically reviewing the current literature, and highlighting unintentional rewilding and spontaneous wildlife comebacks as underused sources of information. Together, these lines of evidence show that trophic cascades may be restored via species reintroductions and ecological replacements. It is clear, however, that megafauna effects may be affected by poorly understood trophic complexity effects and interactions with landscape settings, human activities, and other factors. Unfortunately, empirical research on trophic rewilding is still rare, fragmented, and geographically biased, with the literature dominated by essays and opinion pieces. We highlight the need for applied programs to include hypothesis testing and science-based monitoring, and outline priorities for future research, notably assessing the role of trophic complexity, interplay with landscape settings, land use, and climate change, as well as developing the global scope for rewilding and tools to optimize benefits and reduce human–wildlife conflicts. Finally, we recommend developing a decision framework for species selection, building on functional and phylogenetic information and with attention to the potential contribution from synthetic biology

Megafauna extinction, tree species range reduction, and carbon storage in Amazonian forests

During the Late Pleistocene and early Holocene 59 species of South American megafauna went extinct. Their extinction potentially triggered population declines of large-seeded tree species dispersed by the large-bodied frugivores with which they co-evolved, a theory first proposed by Janzen and Martin (1982). We tested this hypothesis using species range maps for 257 South American tree species, comparing 63 species thought to be primarily distributed by megafauna with 194 distributed by other animals. We found a highly significant (p 95% following disperser extinction. A numerical gap dynamic simulations suggests that over a 10 000 yr period following the disperser extinctions, the average convex hull range size of large-seeded tree species decreased by ∼ 31%, while the estimated decrease in population size was ∼ 54%, indicating a likely greater decrease in species population size than indicated by the empirical range patterns. Finally, we found a positive correlation between seed size and wood density of animal-dispersed tree species implying that the Late Pleistocene and early Holocene megafaunal extinctions reduced carbon content in the Amazon by ∼ 1.5 ± 0.7%. In conclusion, we 1) provide some empirical evidence that megafauna distributed fruit species have a smaller mean range size than wind, water or other animal-dispersed species, 2) demonstrate mathematically that such range reductions are expected from megafauna extinctions ca 12 000 yr ago, and 3) illustrate that these extinctions may have reduced the Amazon's carbon storage capacity

Wild Steps in a semi-wild setting? Habitat selection and behavior of European bison reintroduced to an enclosure in an anthropogenic landscape.

Recently, several wild or semi-wild herds of European bison have been reintroduced across Europe. It is essential for future successful bison reintroductions to know how the European bison use different habitats, which environmental parameters drive their habitat selection, and whether their habitat use and behavioural patterns in new reintroduction sites differ from habitats where European bison have been roaming freely for a long time. Here, we address these questions for a 40-ha enclosed site that has been inhabited by semi-free ranging European bison since 2012. The site, Vorup Meadows, is adjacent to the Gudenå river in Denmark and consists of human-modified riparian meadows. During 2013 we monitored the behavioural pattern and spatial use of the 11 bison present and in parallel carried out floristic analyses to assess habitat structure and food quality in the enclosure. We tested habitat use and selection against environmental parameters such as habitat characteristics, plant community traits, topography, and management area (release area vs. meadow area) using linear regression and spatial models. The bison herd had comparable diurnal activity patterns as observed in previous studies on free-roaming bison herds. Topography emerged as the main predictor of the frequency of occurrence in our spatial models, with high-lying drier areas being used more. Bison did not prefer open areas over areas with tree cover when accounting for habitat availability. However, they spent significantly more time in the release area, a former agricultural field with supplementary fodder, than expected from availability compared to the rest of the enclosure, a meadow with tree patches. We wish to increase awareness of possible long-term ethological effects of the release site and the management protocols accomplished here that might reduce the ecological impact by the bison in the target habitat, and thereby compromise or even oppose the conservation goals of the conservation efforts

Supplement 1. R scripts for trait-level model (TLM) and species-trait-level model (STLM) tests for nonrandom patterns in the functional-diversity–area relationship.

<h2>File List</h2><div> <p><a href="R scripts for calculating FAR null models.r">R scripts for calculating FAR null models.r</a> (MD5: 023dab61d9ade8305be9f11f88cd8753)</p></div><h2>Description</h2><div> <p>R scripts for calculating FAR null models.r contains R scripts for implementing trait-level null model tests (TLM) and species-trait-level null model tests (STLM). Comment lines specify input and its format. The code can be copied and pasted into the R command line interface or called using the source() function. Results are plotted. </p> </div

Appendix B. Testing the null models for the functional-diversity–area relationship.

Testing the null models for the functional-diversity–area relationship

Appendix C. Map of the region used to create the mammalian body size FAR.

Map of the region used to create the mammalian body size FAR

Appendix A. An example of calculation of the null models for the functional-diversity–area relationship.

An example of calculation of the null models for the functional-diversity–area relationship

Estimating the missing species bias in plant trait measurements

Aim: Do plant trait databases represent a biased sample of species, and if so, can that bias be corrected? Ecologists are increasingly collecting and analysing data on plant functional traits, and contributing them to large plant trait databases. Many applications of such databases involve merging trait measurements with other data such as species distributions in vegetation plots; a process that invariably produces matrices with incomplete trait and species data. Typically, missing data are simply ignored and it is assumed that the missing species are missing at random. Methods: Here, we argue that this assumption is unlikely to be valid and propose an approach for estimating the strength of the bias regarding which species are represented in trait databases. The method leverages the fact that, within a given database, some species have many measurements of a trait and others have few (high vs low measurement intensity). In the absence of bias, there should be no relationship between measurement intensity and trait values. We demonstrate themethod using five traits that are part of the TRY database, a global archive of plant traits. Ourmethod also leads naturally to a correction for this bias,which we validate and apply to two examples. Results: Specific leaf area and seed mass were strongly positively biased (frequently measured species had higher trait values than rarely measured species), leaf nitrogen per unit mass and maximum height were moderately negatively biased, and maximum photosynthetic capacity per unit leaf area was weakly negatively biased. The bias-correction method yielded greatly improved estimates in the validation tests for the two most biased traits. Further, in our two applications, ecological interpretations were shown to be sensitive to uncorrected bias in the data. Conclusions: Species inclusion in trait databases appears to be strongly biased in some cases, and failure to correct this can lead to incorrect conclusions