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Thus, we simply represented in a scatterplot the abundance forecast for the future (y-axis) relative to the abundance predicted for the present (x-axis), and visually evaluated if values for the future were over (or under) the diagonal, which represents the situation where the abundance forecast for the future equals that predicted for the present.
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On the one hand, we quantitatively assessed the relationship between C. imicola abundances predicted for the present and abundances forecast for the future.
Thus, two predictions of C. imicola abundance were forecast, one per emission scenario.
In addition, a clear increase in abundance was forecast, demonstrating an effective response of the species to climate change between the study periods.
To forecast wren abundance, we used a meta-population model created from maps of wren abundance, cactus abundance, land use, and estimated fire probability.
This finding suggests that at this time a regression model is not the best means of predicting mosquito abundance; rather, a probabilistic forecast, developed from logistic regression analysis, is more appropriate.
Based on the mutational-burden hypothesis, our estimates of 2 N g μ (i.e. πsilent) in the nuclear and mitochondrial compartments of C. reinhardtii, whether including or excluding the influence of indels, lie too close to the predicted thresholds for intron proliferation in these genomes to accurately forecast intron abundance.
To assess the relative benefit of different post-fire habitat restoration strategies, we forecasted wren abundance over the next 100 years under three restoration strategies: (i) create new "stepping stone" habitat patches to form corridors which connect existing patches, (ii) augment existing habitat patches, and (iii) create new habitat patches in areas with low fire risk.
Another factor that made the first round hard to forecast was the abundance of teams with new coaches or general managers.
The projection of the final models to future climatic scenarios showed that the forecast C. imicola abundance is expected to increase in each locality (Figure 5), whereas its forecast distribution area will increase by a smaller amount (increase rate less than 4.5%).
Before fall sampling in 2007, we forecast bank vole abundance (7 ) by multiplying the spring index (3.16 bank voles/100 trap-nights) with the expected population growth rate from spring to fall.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com