Ecological surprise: concept, synthesis, and social dimensions
Bibliographic record
Abstract
Abstract As the extent and intensity of human impacts on ecosystems increase and the capacity of ecosystems to absorb these impacts dwindles, unanticipated behavior in ecological systems—or surprises—is likely to become more common. The concept of ecological surprise is broadly applied but seldom explicitly developed in ecological literature, and ecologists can employ diverging language, frameworks, and interpretations of surprise. Here, we synthesize what ecological surprise has meant to ecologists studying these events and review the development and use of the concept in ecology. We define ecological surprise as a situation where human expectations or predictions of natural system behavior deviate from observed ecosystem behavior. This can occur when people (1) fail to anticipate change in ecosystems; (2) fail to influence ecosystem behavior as intended; or (3) discover something about an ecosystem that runs counter to accepted knowledge. We develop a conceptual model that captures the interactions between social and ecological processes that lead to these events and examine two types of drivers that contribute to surprise: underlying driving forces and proximate causes. Our definition of ecological surprise inherently acknowledges that, to be surprising, there must be human observers to the ecological occurrence who have expectations about ecosystem behavior. To explore this dimension, we draw on social science perspectives to understand the ways in which human expectations of ecosystems are influenced by social networks, heuristics, and mental models. We use a case study to demonstrate how our integrated conceptualization of ecological surprise provides a systematic way of examining these events. Our integration of these perspectives enables us to better synthesize social and ecological knowledge of these events, and encourages ecologists to critically reflect on how they, as scientists, formulate and reformulate expectations of ecosystem behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".