Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220738022> ?p ?o ?g. }
- W4220738022 abstract "Abstract Near‐term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state‐space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin‐producing cyanobacterium, Gloeotrichia echinulata . We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near‐term (1‐ to 4‐week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4‐week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1‐week‐ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long‐term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems." @default.
- W4220738022 created "2022-04-03" @default.
- W4220738022 creator A5005617262 @default.
- W4220738022 creator A5005897964 @default.
- W4220738022 creator A5006290311 @default.
- W4220738022 creator A5007200683 @default.
- W4220738022 creator A5009240856 @default.
- W4220738022 creator A5016144244 @default.
- W4220738022 creator A5020270454 @default.
- W4220738022 creator A5030647593 @default.
- W4220738022 creator A5035667099 @default.
- W4220738022 creator A5050269082 @default.
- W4220738022 creator A5058192976 @default.
- W4220738022 creator A5058859499 @default.
- W4220738022 creator A5060103502 @default.
- W4220738022 creator A5077820421 @default.
- W4220738022 creator A5078219567 @default.
- W4220738022 date "2022-05-23" @default.
- W4220738022 modified "2023-10-14" @default.
- W4220738022 title "Using near‐term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density" @default.
- W4220738022 cites W127316226 @default.
- W4220738022 cites W1503087350 @default.
- W4220738022 cites W1783324246 @default.
- W4220738022 cites W1789155650 @default.
- W4220738022 cites W1829989199 @default.
- W4220738022 cites W1970405538 @default.
- W4220738022 cites W2004637521 @default.
- W4220738022 cites W2008096686 @default.
- W4220738022 cites W2014426844 @default.
- W4220738022 cites W2025433497 @default.
- W4220738022 cites W2027234700 @default.
- W4220738022 cites W2028110514 @default.
- W4220738022 cites W2028813975 @default.
- W4220738022 cites W2039676732 @default.
- W4220738022 cites W2043491182 @default.
- W4220738022 cites W2044286222 @default.
- W4220738022 cites W2055162110 @default.
- W4220738022 cites W2061487852 @default.
- W4220738022 cites W2069462603 @default.
- W4220738022 cites W2070056097 @default.
- W4220738022 cites W2075207275 @default.
- W4220738022 cites W2075784822 @default.
- W4220738022 cites W2079237780 @default.
- W4220738022 cites W2080042677 @default.
- W4220738022 cites W2083680853 @default.
- W4220738022 cites W2086001701 @default.
- W4220738022 cites W2086078378 @default.
- W4220738022 cites W209407419 @default.
- W4220738022 cites W2103289699 @default.
- W4220738022 cites W2103478651 @default.
- W4220738022 cites W2104990636 @default.
- W4220738022 cites W2105953614 @default.
- W4220738022 cites W2113162751 @default.
- W4220738022 cites W2114548855 @default.
- W4220738022 cites W2117062091 @default.
- W4220738022 cites W2120514850 @default.
- W4220738022 cites W2121291834 @default.
- W4220738022 cites W2122089686 @default.
- W4220738022 cites W2127874106 @default.
- W4220738022 cites W2129930334 @default.
- W4220738022 cites W2130985504 @default.
- W4220738022 cites W2132766973 @default.
- W4220738022 cites W2133011501 @default.
- W4220738022 cites W2137132328 @default.
- W4220738022 cites W2142157592 @default.
- W4220738022 cites W2142733079 @default.
- W4220738022 cites W2146879263 @default.
- W4220738022 cites W2148751263 @default.
- W4220738022 cites W2160033478 @default.
- W4220738022 cites W2162963610 @default.
- W4220738022 cites W2176091847 @default.
- W4220738022 cites W2292612048 @default.
- W4220738022 cites W2301969289 @default.
- W4220738022 cites W2302501749 @default.
- W4220738022 cites W2476077119 @default.
- W4220738022 cites W2495438233 @default.
- W4220738022 cites W2511618397 @default.
- W4220738022 cites W2536166122 @default.
- W4220738022 cites W2562502911 @default.
- W4220738022 cites W2573491640 @default.
- W4220738022 cites W2594325430 @default.
- W4220738022 cites W2613625243 @default.
- W4220738022 cites W2620708285 @default.
- W4220738022 cites W2648432042 @default.
- W4220738022 cites W2770096046 @default.
- W4220738022 cites W2786425804 @default.
- W4220738022 cites W2786801025 @default.
- W4220738022 cites W2803029281 @default.
- W4220738022 cites W2816161969 @default.
- W4220738022 cites W2892386441 @default.
- W4220738022 cites W2893465490 @default.
- W4220738022 cites W2898601766 @default.
- W4220738022 cites W2932493822 @default.
- W4220738022 cites W2988205563 @default.
- W4220738022 cites W2989657264 @default.
- W4220738022 cites W3008732022 @default.
- W4220738022 cites W3026678275 @default.
- W4220738022 cites W3027646416 @default.
- W4220738022 cites W3031519568 @default.
- W4220738022 cites W3047339303 @default.