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We consider Bandicoot's main usage to be prediction of population characteristics at an individual level.
Understanding the patterns of movement and breeding within and between wildlife populations is important for the assessment of conservation status of endangered species, the development of conservation management strategies and priorities, and the prediction of population behaviour based on future threats.
For example, despite our overall prediction of population decline with barrier construction, our results are more conservative than were previous estimates generated by the simpler mean field model developed by Owen-Smith [17].
Robust computational approaches are available to incorporate these multiple sources of stochasticity and heterogeneity, which enable prediction of population behavior.
For interventions that have never been implemented, on the other hand, quantitative prediction of population health impact may not be possible with any useful degree of precision.
For some types of interventions, it may be appropriate to demand that applicants provide a quantitative prediction of population health impact.
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In analogy to the analysis that followed from the Lotka-Euler characteristic equation, we base our predictions of population growth on equation (13) and the function σ.
Earlier models use time-independent vital rates and make predictions of population growth or decline by solving and analyzing the so-called Lotka-Euler characteristic equation, int_{0}^{infty}Q(a)e^{-ka},da=1.
In contrast, predictions of population growth rate varied most among landscapes with only enough clustered habitat to support a small population, i.e., near a spatially mediated extinction threshold.
Considering those refined levels, The Ethnologue provided better predictions of population genetic structure than Greenberg's classification.
Our analysis indicates that linear and non-linear models may, in fact, lead to significantly different predictions of population response to climate change.
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