Predicting aquatic invasion in Adirondack lakes: a spatial analysis of lake and landscape characteristics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Invasive species continue to pose major challenges for managing coupled human–environmental systems. Predictive tools are essential to maximize invasion monitoring and conservation efforts in regions reliant on abundant freshwater resources to sustain economic welfare, social equity, and ecological services. Past studies have revealed biotic and abiotic heterogeneity, along with human activity, can account for much of the spatial variability of aquatic invaders; however, improvements remain. This study was created to (1) examine the distribution of aquatic invasive species richness ( AISR ) across 126 lakes in the Adirondack Region of New York; (2) develop and compare global and local models between lake and landscape characteristics and AISR ; and (3) use geographically weighted regression ( GWR ) to evaluate non‐stationarity of local relationships, and assess its use for prioritizing lakes at risk to invasion. The evaluation index, AISR , was calculated by summing the following potential aquatic invaders for each lake: Asian Clam ( Corbicula fluminea ), Brittle Naiad ( Najas minor ), Curly‐leaf Pondweed ( Potamogeton crispus ), Eurasian Watermilfoil ( Myriophyllum spicatum ), European Frog‐bit ( Hydrocharis morsus‐ranae ), Fanwort ( Cabomba caroliniana ), Spiny Waterflea ( Bythotrephes longimanus ), Variable‐leaf Milfoil ( Myriophyllum heterophyllum ), Water Chestnut ( Trapa natans ), Yellow Floating Heart ( Nymphoides peltata ), and Zebra Mussel ( Dreissena polymorpha ). The Getis‐Ord Gi* statistic displayed significant spatial hot and cold spots of AISR across Adirondack lakes. Spearman's rank (ρ) correlation coefficient test ( r s ) revealed urban land cover composition, lake elevation, relative patch richness, and abundance of game fish were the strongest predictors of aquatic invasion. Five multiple regression global Poisson and GWR models were made, with GWR fitting AISR very well ( R 2 = 76–83%). Local pseudo‐ t ‐statistics of key explanatory variables were mapped and related to AISR , confirming the importance of GWR for understanding spatial relationships of invasion. The top 20 lakes at risk to future invasion were identified and ranked by summing the five GWR predictive estimates. The results inform that inexpensive and publicly accessible lake and landscape data, typically available from digital repositories within local environmental agencies, can be used to develop predictions of aquatic invasion with remarkable agreement. Ultimately, this transferable modeling approach can improve monitoring and management strategies for slowing the spread of invading species.
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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 | 0.000 |
| 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 it