An evidence map of research assessing the effects of timber harvesting on water quality, biotic and biodiversity indicators in running waters
Bibliographic record
Abstract
Freshwater quality and biodiversity are known to be affected by surrounding timber harvesting activities. However, variable impacts across studies make it difficult to predict the implications of harvesting for freshwaters. Evidence syntheses compile existing research to assess whether robust predictions of impact can be drawn and determine where gaps lie. Yet, no synthesis that we know of describes the overall evidence landscape of research assessing the effects of forest management for wood production (hereafter: timber harvesting) on water quality and aquatic biodiversity of running waters. We address this gap by creating an evidence map specifically focused on boreal and temperate biomes - which contribute heavily to timber production. Using Web of Science Core Collections, Scopus and Google Scholar, we located 638 relevant publications from which we identified three key primary research and evidence synthesis priorities focused on prediction of impact using existing literature. Most studies took place in the United States of America (56 %, n = 358) and quantified two or more biotic or water quality indicators (range = 1–52, mean = 7, sd = 7). Water quality was more frequently assessed across studies (80 %, n = 511) than biotic indicators (39 %, n = 248), with benthic macroinvertebrates being the most commonly assessed taxon (50 % of studies that quantified biotic indicators, n = 124). Biodiversity-specific biotic indicators (e.g. richness) were assessed at a similar frequency (51 % of all biotic indicator measurements, n = 606) to other types of biotic indicators (e.g. abundance) (49 %, n = 594). The majority of studies that contained temporal information collected data about water quality and biotic indicators for no longer than five years (56 %, n = 358) and no more than five years after timber harvesting events (66 %, n = 309 studies). Although numerous studies contained no information about the types of harvesting in their study regions (19 %, n = 122), those that did mainly focused on effects of clearcutting (n = 458 studies). Most studies did not contain watershed-scale information about timber harvesting (58 %, n = 349). Together, these findings point toward three key primary research priorities which include: capturing a broader scope of effects, especially regarding biodiversity and other biotic indicators; increasing our ability to detect long-term changes related to timber harvesting; and, better accounting for watershed level processes. We provide suggestions for approaches to address each of these research priorities and examples of how evidence syntheses that utilize and build on the dataset we compiled for this map could improve understanding and prediction of the effects of timber harvesting on fresh waters. • Indicators of water quality are more frequently assessed than biotic indicators, including biodiversity metrics. • We know little about the long-term effects of timber harvesting on fresh waters. • Over half the research in this area lacks watershed-scale information. • Sufficient data are published to allow meta-analyses to fill several gaps. • Emerging technologies like environmental DNA can be used to address gaps.
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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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.081 | 0.081 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".