DOI: 10.5061/dryad.7jf0vp5
title: Data from: The dynamics of open populations: integration of top–down, bottom–up and supply‐side influences on intertidal oysters

description: Most communities are structured not by a single process but by some
 combination of top–down, bottom–up and supply‐side (i.e. juvenile
 recruitment) factors. However, establishing how multiple processes
 interact remains a fundamental challenge. For example, the recruitment,
 growth, and mortality of estuarine species can vary along the steep and
 numerous environmental and biological gradients typical of these habitats,
 but the relative importance of those gradients is generally unknown. We
 took a novel approach to this question by coupling long‐term field
 observations of the Olympia oyster Ostrea lurida in a central California
 estuary with a state‐space integral projection model. This approach
 revealed that the most parsimonious description of oyster population
 dynamics involved spatial variation in growth and adult mortality – but
 not juvenile mortality – as well as spatiotemporal variation in
 recruitment. These patterns match the available short‐term estimates of
 each of those processes from field studies, and reveal a synthetic view of
 oyster population dynamics. Larval recruitment has an interannual ‘boom
 and bust’ pattern, and during good recruitment years most larvae settle in
 the inner bay where water residence time is highest. Adult oyster
 mortality is also highest in the inner bay, where several invasive
 predators are abundant and lowest in the mid‐bay, where oyster growth is
 greatest (due to bottom–up factors), likely leading to a size refuge from
 native predators. Surprisingly, juvenile mortality was constant across the
 bay, possibly because of a lack of size refuge from native and invasive
 predators. Our research approach represents an important advance in
 disentangling the contributions of spatio–temporal variation in top–down,
 bottom–up and supply‐side forces to the dynamics of populations with open
 recruitment.
description: Oyster size distribution
 and abundance dataThis file contains the
 raw survey data from size-abundance surveys at each site from 2004-2009.
 Each row corresponds to an observation of a single oyster on one face of
 one rock on one transect at a site in a given year. If multiple oysters
 were observed on a given rock face, each oyster is listed on a separate
 row with the same spatial label. Full meta-data can be accessed at the
 following GitHub site:
 https://github.com/jwilsonwhite/IPM_statespace/tree/master/Kimbro_etal_OikosActual_Size_Data.csvPost-settlement mortality experimentResults of experiment that evaluated post-settlement mortality of oysters in Tomales Bay, CA. Results of the experiment are illustrated in Figure 1. In file, column 1 "site" refers to each individual site and is a factor. Column 2, "region" refers to outer, mid, or inner area of Tomales Bay. Column 3 "unit" refers to individual experimental unit randomly assigned among sites. Column 4 "treatment" refers to one of three levels of the factor predation. Column 5 "number" refers to number of oysters on tile. Column 6 "revised" refers to corrected number of oysters on tiles.Column 7 "surv" refers to proportional survival at end of experiment (initial/final oysters). Columns 8 and 9 refer to number of alive and dead oysters. Column 10 refers to distance (km) of site from mouth of the bay. Column 11 "mort" refers to proportional mortality of oysters (1-initial/final).postsettlementM2011.csvR code for analysis of Post-settlement mortality experimentR code (r script) that contains code used to analyze post-settlement oyster mortality experiment and produce associated figure (Figure 3)Post_settlement_Experiment2011.Raggregate-recruitment-Jan2018This file contains the mean and standard deviation of the number of oyster spat observed on 0.25 m2 tiles deployed at monitoring stations in Tomales Bay. Each row indicates the annual average for a particular monitoring station, labeled by its distance from the mouth of the bay. These data comprise the ‘field data’ displayed in Fig. 2D. All related code and meta-data can be accessed at the GitHub site https://github.com/jwilsonwhite/IPM_statespace/tree/master/Kimbro_etal_Oikosfit_vonBert_growthThis file is a Matlab function that uses nonlinear least squares to fit von Bertalanffy growth curves to the data for each site in Tomales_site_growth_24Jan2018.csv. The function also plots the growth curves shown in Appendix 3. Please see GitHub site for further meta-data. https://github.com/jwilsonwhite/IPM_statespace/tree/master/Kimbro_etal_Oikossize_density_figures(Fig1)This file is a Matlab function that creates the plots of oyster density and size over space in each year that comprise Fig. 1B,C in the manuscript. Please see GitHub site for further meta-data. https://github.com/jwilsonwhite/IPM_statespace/tree/master/Kimbro_etal_Oikossort_oysterdata_IPMThis file converts the raw data in Actual_Size_Data.csv into a size distribution for each site and year, standardizing for survey area and survey effort in each year. See GitHub link for further meta-data and any updates. https://github.com/jwilsonwhite/IPM_statespace/tree/master/Kimbro_etal_OikosTomales_site_growth_24Jan2018This file contains the mean and standard deviation of length of oysters from juvenile outplant experiment described in Appendix 3. Each row corresponds to an observation at a site (labeled using the scheme indicated in Fig. 1), year, and age in months from the initial deployment of spat.Tomales_Size_Density_2004-2009This file contains the mean and standard deviation of oyster density (number per 0.25 m2) and oyster length (mm) measured in population surveys from 2004-2009. Sites are labeled using the scheme indicated in Fig. 1, and are also listed by distance (km) from the mouth of Tomales bay.