Detecting Amphibians in Agricultural Landscapes Using Environmental DNA Reveals the Importance of Wetland Condition
Bibliographic record
Abstract
Abstract Amphibians are declining worldwide, in part because of large-scale degradation of habitat from agriculture and pervasive pathogens. Yet a common North American amphibian, the wood frog (Lithobates sylvaticus), ranges widely and persists in agricultural landscapes. Conventional survey techniques rely on visual encounters and dip-netting efforts, but detectability limits the ability to test for the effects of environmental variables on amphibian habitat suitability. We used environmental DNA to determine the presence of wood frogs and an amphibian pathogen (ranavirus) in Prairie Pothole wetlands and investigated the effects of 32 water quality, wetland habitat, and landscape-level variables on frog presence at sites representing different degrees of agricultural intensity. Several wetland variables influenced wood frog presence, the most influential being those associated with wetland productivity (i.e., nutrients), vegetation buffer width, and proportion of the surrounding landscape that is comprised of other water bodies. Wood frog presence was positively associated with higher dissolved phosphorus (>0.4 mg/L), moderate dissolved nitrogen (0.1–0.2 mg/L), lower chlorophyll a (≤15 µg/L), wider vegetation buffers (≥10 m), and more water on the landscape (≥0.25). These results highlight the effects of environmental factors at multiple scales on the presence of amphibians in this highly modified landscape—namely the importance of maintaining wetland water quality, vegetation buffers, and surrounding habitat heterogeneity. Environ Toxicol Chem 2019;38:2750–2763. © 2019 SETAC Abstract A graphical summary illustrating that, although detection of wood frogs did not differ between site types (grassland or cropland), a number of factors at different scales influenced detection likelihood (shown as more or fewer frogs). Positive wood frog detection was associated with greater proportion of water and wetlands on the landscape, larger vegetation buffers, higher dissolved phosphorus, a narrow range of dissolved nitrogen, and lower chlorophyll a concentrations. Water bottle image obtained from MindtheGraph.com. Chl. a = chlorophyll a; DN = dissolved nitrogen; DP = dissolved phosphorus.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".