Kernel density estimation (KDE) is a cornerstone of non-parametric statistics, offering a flexible means to infer an underlying probability density from finite samples without assuming a predetermined ...
Identifying the general rules underlying density dependence requires quantifying density’s relationship with proxies of interaction rates at fine scales across a diversity of systems, then identifying ...
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