Manuel--
I apologize in advance for not answering the exact question you ask about packages. [It is included in some geostatistics packages in terms of semivariance, nugget, sill, etc.]
In ecological data, time to independence is very scale dependent. There's autocorrelation at scales of seconds due to instrument temperature-dependence if that hasn't been calibrated for, or the same individuals in the camera trap frame. That component of dependence may have a half-life of minutes. There's often autocorrelation based on time of day & temperature, with cycles of 24 hours. There may be pulse events from storms that persist a few days. There's seasonality driving temperatures, day lengths, and plant & animal behavior, with cycles of 1 year. Then where I live there are ENSO-driven temporal dependence at scales of 1.5 - 3 years, PDO at about a decade, and ENSO-La Nina dominated periods of 4-6 decades that drive not just ocean ecology, but rainfall & thus terrestrial ecology. Then there's tends up to climate change.
So, in my experience in optimizing sampling designs for monitoring for trends, the majority of the temporal dependence is driven by cycles or pulses of characteristic duration, and that is more useful for determining the sampling frequency than empirical estimation form a "continuous" datastream of limited duration. That approach also helps me think about the spatial concordance of the correlated errors: which are site-specific, which are concordant across all of the sites.
Tom
-----Original Message-----
From: R-sig-ecology <r-sig-ecology-bounces at r-project.org> On Behalf Of Manuel Sp?nola
Sent: Wednesday, March 3, 2021 1:06 PM
To: r-sig-ecology at r-project.org
Subject: [EXTERNAL] [R-sig-eco] Time to Independence in R
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Dear list members,
It is common in ecology to sampling in almost a continuous manner when using data loggers, camera traps, sound recorders, gps radio-collars. etc.
Is there any R package to assess time to independence for the data to avoid temporal autocorrelation?
I know that there are models to take into account the temporal autocorrelation of the data, but I am asking to optimize the data collection, before modeling.
Thank you very much in advance.
Manuel
--
*Manuel Sp?nola, Ph.D.*
Instituto Internacional en Conservaci?n y Manejo de Vida Silvestre Universidad Nacional Apartado 1350-3000 Heredia COSTA RICA mspinola at una.cr <mspinola at una.ac.cr> mspinola10 at gmail.com
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