The new diversity: management gains through insights into the functional diversity of communities
Bibliographic record
Abstract
Summary 1. Biodiversity is being lost in our rapidly changing world. Most studies that examine biodiversity measure species richness, but loss of functional diversity (FD) – defined as the trait variation or dispersion in an assemblage – is equally important. FD is useful for management actions focused on ecosystem services or functions, especially when ecosystem function is ambiguously defined, or there are multiple functions of interest. Because FD is often a multivariate measure of species differences, it has additional relevance given that we seldom have a complete understanding of how traits translate to function. 2. This Special Profile includes six papers that examine how management activities or policy could benefit from consideration of the functional contributions of species. When focused on species richness, management activities may unintentionally reduce FD, with detrimental consequences for ecosystem services or stability. For example, two of the studies included in this Special Profile show that grassland management (e.g. mowing or grazing) can result in loss of FD, which may result in an unintended loss of ecosystem services. 3. A third paper reviews how species extinction and environmental change can negatively affect FD and, potentially, ecosystem function. At the individual species level, invasions offer insight into the importance of species traits. The fourth paper in this Special Profile describes how an invader is successful because of unique trait values, underlining the importance of examining FD when considering management options. In another paper, eutrophication is shown to drastically affect lichen functional groups (some decreasing and some increasing) with a minor effect on overall richness. The final paper demonstrates that the ability of aquatic plants to maintain biomass production in changing environments is determined by their complementary contributions to productivity. These complementary contributions to ecosystem function –especially given that they are more important in fluctuating environments – need to be included in evaluations of diversity. 4. Synthesis and applications . As FD is increasingly assessed in applied studies, it will call into question how we measure diversity and evaluate management success. It is clear that species richness cannot be the only measure considered. Given that environmental change and management activities can reduce FD as well as the number of species, management goals and criteria for success need to include FD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".