CODrecovery

A dataset containing three simulated cod populations. Community 1 does not recovery whereas Community 100 and 200 do.

datasets

Rolling and expanding window approaches to assessing abundance based early warning signals, non-equilibrium resilience measures, and machine learning. See Dakos et al. (2012) <doi:10.1371/journal.pone.0041010>, Deb et al. (2022) <doi:10.1098/rsos.211475>, Drake and Griffen (2010) <doi:10.1038/nature09389>, Ushio et al. (2018) <doi:10.1038/nature25504> and Weinans et al. (2021) <doi:10.1038/s41598-021-87839-y> for methodological details. Graphical presentation of the outputs are also provided for clear and publishable figures. Visit the 'EWSmethods' website for more information, and tutorials.

Duncan O'Brien

EWSmethods

Forecasting Tipping Points at the Community Level

Smita Deb

Sahil Sidheekh

Narayanan Krishnan

Partha Dutta

Christopher Clements

CODrecovery function

A list of three dataframes with 191 rows and 6 variables each:<dl>
 <dt>community_id</dt>
<dd>the identity of the simulated community</dd> <dt>time</dt>
<dd>time index</dd> <dt>biomass</dt>
<dd>population total biomass</dd> <dt>mean.size</dt>
<dd>average length of cod individuals</dd> <dt>sd.size</dt>
<dd>variation in length of cod individuals</dd> <dt>inflection_pt</dt>
<dd>the time index where transition occurs</dd>
</dl>

Format

Three Recovering Cod Populations — CODrecovery

A list of three dataframes with 191 rows and 6 variables each:<dl>
 <dt>community_id</dt>
<dd>the identity of the simulated community</dd>

 <dt>time</dt>
<dd>time index</dd>

 <dt>biomass</dt>
<dd>population total biomass</dd>

 <dt>mean.size</dt>
<dd>average length of cod individuals</dd>

 <dt>sd.size</dt>
<dd>variation in length of cod individuals</dd>

 <dt>inflection_pt</dt>
<dd>the time index where transition occurs</dd>


</dl>

CODrecovery: Three Recovering Cod Populations

Description

Usage

Arguments

Format

Examples