DOI: 10.5061/dryad.msbcc2g5n
title: Data from: Natural coral recovery despite negative population growth

description: Demographic processes that ensure the recovery and resilience of marine
 populations are critical as climate change sends an increasing proportion
 on a trajectory of decline. Yet for some populations, recovery potential
 remains high. We conducted annual monitoring over 9-years (2012–2020) to
 assess the recovery of coral populations belonging to genus Pocillopora.
 These populations experienced a catastrophic collapse following a severe
 typhoon in 2009. From the start of the monitoring period, high initial
 recruitment led to the establishment of a juvenile population that rapidly
 transitioned to sexually mature adults, which dominated the population
 within six years after the disturbance. As a result, coral cover increased
 from 1.1% to 20.2% during this time. To identify key demographic drivers
 of recovery and population growth rates (λ), we applied kernel resampled
 Integral Projection Models (IPMs), constructing eight successive models to
 examine annual change. IPMs were able to capture reproductive traits as
 key demographic drivers over the initial 3 years, whilst individual growth
 was a continuous key demographic driver throughout the entire monitoring
 period. IPMs further detected a pulse of reproductive output subsequent to
 two further Category 5 typhoon events during the monitoring period,
 exemplifying key mechanisms of resilience for coral populations impacted
 by disturbance. Despite rapid recovery, (i.e., increased coral cover,
 individual colony growth, low mortality), IPMs estimated predominantly
 negative values of λ, indicating a declining population. Indeed, whilst λ
 translates to a change in the number of individuals, the recovery of coral
 populations can also be driven by an increase in the size of coral
 surviving colonies. Our results illustrate that accumulating long-term
 data of historical dynamics and applying IPMs to extract demographic
 drivers are crucial for future predictions that are based on comprehensive
 and robust understandings of ecological change. 
description: <strong>Data collection </strong>
 Orchid Island (22°03′N, 121°32′E) is a 45
 km<sup>2</sup> volcanic, tropical island 64 km off the coast
 of Taiwan, encircled by a narrow fringing reef (5–10 m depth), leading to
 a dramatic drop-off. Such reef topography is sensitive to typhoons that
 are both frequent and intense in the region (Ribas-Deulofeu et al., 2021).
 In 2009, the island was severely affected by Typhoon Morakot (Hall et al.,
 2013), the deadliest typhoon to hit Taiwan in recorded history, which
 caused a ~66% decline in mean live coral cover (~60% to ~20%) along reefs
 in southern Taiwan (Kuo et al., 2011).<br>          Three years
 after this major disturbance in 2012, three parallel 20 m transects were
 established at ~8 m depth spaced ~2.5 m apart at a site to the southwest
 of the island (named Green Grassland; 22°00'N 121°34'E).
 Usually, this reef site is relatively sheltered from both the waves
 generated by the winter north-easterly monsoon and summer south-westerly
 winds. However, on this occasion was proven susceptible to the typhoon in
 2009, impacted by a west to south-westerly swell. To the side of each
 transect, 50 cm × 50 cm permanent quadrats (n = 11) were haphazardly
 positioned to assess demographic changes over time (Appendix S1: Figure S1
 and Figure S2). A total of 33 quadrats were made permanent by placing
 markers (iron pegs) at each corner with tags attached indicating the
 designated identification number, making the quadrat easier to detect for
 future monitoring. With this method, only four quadrats were lost in
 subsequent surveys, whereas the position of the other quadrats (n = 29)
 remained. However, the precise location may slightly vary due to the
 ever-changing dynamics of the reef. For this reason, when quadrats were
 placed at each marked position, a wide-scale community picture was taken
 ca. 2 m above the substrate, in order to correct any error in positioning
 during the analysis, if necessary (Appendix S1: Figure S1a). The quadrat
 was then photographed at a higher-resolution ca. 1 m above the substrate
 in order to capture the overall benthic composition (Appendix S1: Figure
 S1b). The quadrat was then divided into four sections, which were
 individually captured in order to attain high-resolution images of
 individual colonies (Appendix S1: Figure S1c). In each of these sections,
 smaller sized individuals (ca. &lt; 5 cm in length) were photographed
 with close-up images and scale. This protocol was repeated annually
 between 2012 and 2020. From photographs of the 29 permanent quadrats,
 every colony (n = 336) observed was first measured for its two-dimensional
 (2D) colony size (projected area) using Image J software (Schneider,
 Rasband and Eliceiri, 2012). The 2D colony size was used for the analysis
 of annual change in coral cover. Coral cover (%) was determined by
 measuring the sum of 2D projected areas of coral colonies relative to the
 area of all quadrats combined. All quadrats were originally occupied by
 <em>Pocillopora</em>, but from 2016 to the end of the
 monitoring period, 1 quadrat was empty of <em>Pocillopora</em>
 colonies with the cover calculation still taking this area into
 consideration. In the rare case of a slight overlap of colonies (n = 4),
 the 2D projected area could be easily deduced for the unseen part of
 colonies. <strong>Measurement of demographic
 vital rates</strong> In this study, we focused on
 locally dominant genus <em>Pocillopora </em>spp. The relative
 contribution of species to the <em>Pocillopora </em>complex
 was genetically examined by randomly sampling colonies at the site and
 barcoding mtORF region after extraction of genomic DNA (Johnston, Forsman
 and Toonen, 2018). Out of 31 sampled <em>Pocillopora
 </em>colonies, 17 were <em>P. verrucosa </em>and 14 were
 <em>P. meandrina</em> (Appendix S1: Table S1 and S2). Besides
 the two dominant <em>Pocillopora </em>species, there were at
 least two other <em>Pocillopora </em>species present;
 <em>Pocillopora eydouxi</em> and
 <em>Pocillopora</em> sp. These species are broadcast-spawners,
 with the exception of<em> Pocillopora</em> sp., which is a
 brooder (Mulla et al. 2021). Due to the difficulty in identifying species
 morphologically in the field, especially at the early life stages, we
 treated species as a <em>Pocillopora</em> complex<em>
 </em>(<em>Pocillopora</em> populations).
             As corals are 3D structures, colony size (surface
 area) was used for <em>Pocillopora</em> colonies in the IPMs,
 which allowed us to build higher-resolution models. 3D surface area
 (cm<sup>2</sup>) was allometric and estimated from 2D
 projections using a pre-established relationship. Detailed information on
 the 2D to 3D conversion can be found in Appendix Figure S3. We extracted
 information on colony growth, survival and recruitment of
 <em>Pocillopora </em>populations over the 9-year period using
 size-thresholds in 3D to distinguish visible recruits (0.4–10
 cm<sup>2</sup>; n = 154), juveniles (10.1–100
 cm<sup>2</sup>; n = 369) and adults (&gt; 100.1
 cm<sup>2</sup>; n = 532). These threshold for visible recruits
 was determined from the size range of newly appearing individuals from
 each year from the second year of monitoring. The threshold for juveniles
 was determined by the maximum size of visible recruits and the minimum
 size of sexually mature individuals (described in more detail below).
 These thresholds differentiate sexually immature (visible
 recruits/juveniles) to mature (adults) individuals, used for ecological
 interpretation.<br>            To identify size-specific
 relationships of demographic traits associated with reproduction, two
 nubbins (~5 cm in branch length) were collected from 40 colonies of
 varying size of <em>P. verrucosa</em> (probably including
 <em>P. meandrina</em>: 68.2–685.8 cm<sup>2</sup>
 in 3D size) during the reproductive season (April, 2017) at neighbouring
 Green Island (Lin and Nozawa, 2017). In addition, a further 20 nubbins (of
 the same size) were collected (68.2–364.7 cm<sup>2</sup> in 3D
 size) to determine the minimum size of sexual maturity at the same time.
 Nubbins were fixed in a 10% formalin-seawater solution and examined using
 standard histological methods. Tissue of nubbins were decalcified and
 dehydrated in an alcohol series using a tissue processor (Thermo
 Scientific, Excelsior ES, USA) and embedded in paraffin wax (Thermo
 Scientific Histoplast PE, USA). Samples were then cut with a microtome
 (Thermo Scientific, Finesse 325) at 6 µm thick intervals. Xylene was used
 to deparaffin samples and tissue sections were mounted on glass slides,
 stained with hematoxylin and eosin using a staining machine (Shandon
 Varistain, Thermo Scientific, USA) and then preserved with
 Organol/Limonene mounting medium and a glass cover. Sections were examined
 under a BX51 light microscope (Olympus, Japan). For each nubbin, 2 polyps
 were haphazardly chosen and the number of oocytes per polyp was determined
 by observing the entire section of each polyp (a total of 4 polyps per
 colony). The probability of a colony being reproductively active was
 determined by the presence or absence of oocytes over colony
 size. 
description: # Data from: Natural coral recovery despite negative population growth ---
 This Mullaetal_2024_dataset_README.txt file was generated on 2023-11-09 by
 AJM ([zeezyuk@gmail.com](mailto:zeezyuk@gmail.com)). DOI:
 10.5061/dryad.msbcc2g5n Abstract Demographic processes that ensure the
 recovery and resilience of marine populations are critical as climate
 change sends an increasing proportion on a trajectory of decline. Yet for
 some populations, recovery potential remains high. We conducted annual
 monitoring over 9-years (2012–2020) to assess the recovery of coral
 populations belonging to genus Pocillopora. These populations experienced
 a catastrophic collapse following a severe typhoon in 2009. From the start
 of the monitoring period, high initial recruitment led to the
 establishment of a juvenile population that rapidly transitioned to
 sexually mature adults, which dominated the population within six years
 after the disturbance. As a result, coral cover increased from 1.1% to
 20.2% during this time. To identify key demographic drivers of recovery
 and population growth rates (λ), we applied kernel resampled Integral
 Projection Models (IPMs), constructing eight successive models to examine
 annual change. IPMs were able to capture reproductive traits as key
 demographic drivers over the initial 3 years, whilst individual growth was
 a continuous key demographic driver throughout the entire monitoring
 period. IPMs further detected a pulse of reproductive output subsequent to
 two further Category 5 typhoon events during the monitoring period,
 exemplifying key mechanisms of resilience for coral populations impacted
 by disturbance. Despite rapid recovery, (i.e., increased coral cover,
 individual colony growth, low mortality), IPMs estimated predominantly
 negative values of λ, indicating a declining population. Indeed, whilst λ
 translates to a change in the number of individuals, the recovery of coral
 populations can also be driven by an increase in the size of coral
 surviving colonies. Our results illustrate that accumulating long-term
 data of historical dynamics and applying IPMs to extract demographic
 drivers are crucial for future predictions that are based on comprehensive
 and robust understandings of ecological change. **Author Information**
 First & corresponding author Name: Dr Aziz J Mulla Institution:
 Biodiversity Research Center, Academia Sinica, Taipei, Taiwan Email:
 [zeezyuk@gmail.com](mailto:zeezyuk@gmail.com) Second author Name: Dr
 Vianney Denis Institution: Institute of Oceanography, National Taiwan
 University, Taipei 10617, Taiwan Third author Name: Dr Che-Hung Lin
 Institution: Department of Aquatic Biosciences, National Chiayi
 University, Chiayi City, Taiwan Fourth author Name: Chia-Ling Fong
 Institution: Biodiversity Research Center, Academia Sinica, Nangang,
 Taipei, Taiwan Fifth author Name: Dr Jia-Ho Shiu Institution: General
 Research Service Center, National Pingtung University of Science and
 Technology, Pingtung, Taiwan Sixth author Name: Dr Yoko Nozawa
 Institution: Tropical Biosphere Research Center, University of the
 Ryukyus, Okinawa, Japan 1. **Date of data collection**: 2012-2020 2.
 **Geographic location of data collection**: Orchid Island, southern
 Taiwan, west Pacific 3. **Funding sources that supported the collection of
 the data**: Internal Academia Sinica Research Grant 4. **Recommended
 citation for this dataset** Mulla et al (2024), Data from: Natural coral
 recovery despite negative population growth , Dryad, Dataset, DOI ####
 DESCRIPTION OF THE DATA AND FILE STRUCTURE ##### DATA & FILE OVERVIEW
 **Description of dataset** Both Data_XX_XX and Repro_data should be
 uploaded together when in use for IPMs but can be used separately. In
 Data_XX_XX, there are 6 columns: 'plot' is the location the data
 was collected, 'idcoral' is the identification of the coral
 which can be used to track the same individual through time,
 'species' is the Pocillopora complex used in this study,
 'size_XXXX' is a list of all the colony sizes in that year,
 'size_XXXX' is the same but for XXXX and 'survive' is
 the survival outcome of each colony [1 = survive, 0 = dead, NA = New
 recruit in the latter year]. In the Repro_data file there are 10 columns:
 'species' which denotes the Pocillopora complex assessed,
 'size-range' the sizes of colonies split between the
 'min' and 'max' size recorded, 'n' is the
 number of individuals within those size ranges, 'diameter_avg'
 and 'diameter_sd', is the average diameter of colonies in the
 size ranges and standard deviations, 'col_fertility', is the
 prop of colonies fecund, 'pol_fertility' and
 'pol_fertility_sd' are the % of fertile polyps. Code partially
 adapted from Shlesinger and van Woesik, 2021 **File List** File 1:
 Mullaetal_dataset_Orchid_1220.csv File 1 description: Full dataset
 (2D&3D measurements) from permanent quadrats between 2012-2020 File 2:
 Data_12_13.csv File 2 description: 2012-2013 log-transformed 3D data for
 use in IPMs File 3: Data_13_14.csv File 3 description: 2013-2014
 log-transformed 3D data for use in IPMs File 4: Data_14_15.csv File 4
 description: 2014-2015 log-transformed 3D data for use in IPMs File 5:
 Data_15_16.csv File 5 description: 2015-2016 log-transformed 3D data for
 use in IPMs File 6: Data_16_17.csv File 6 description: 2016-2017
 log-transformed 3D data for use in IPMs File 7: Data_17_18.csv File 7
 description: 2017-2018 log-transformed 3D data for use in IPMs File 8:
 Data_18_19.csv File 8 description: 2018-2019 log-transformed 3D data for
 use in IPMs File 9: Data_19_20.csv File 9 description: 2019-2020
 log-transformed 3D data for use in IPMs File 10: Repro_data.csv File 10
 description: Reproduction data for Pocillopora colonies of different sizes
 File 11: IPM_script_12_13.R File 11 description: R code for IPM analyses
 between 2012-2013 File 12: IPM_script_13_14.R File 12 description: R code
 for IPM analyses between 2013-2014 File 13: IPM_script_14_15.R File 13
 description: R code for IPM analyses between 2014-2015 File 14:
 IPM_script_15_16.R File 14 description: R code for IPM analyses between
 2015-2016 File 15: IPM_script_16_17.R File 15 description: R code for IPM
 analyses between 2016-2017 File 16: IPM_script_17_18.R File 16
 description: R code for IPM analyses between 2017-2018 File 17:
 IPM_script_18_19.R File 17 description: R code for IPM analyses between
 2018-2019 File 18: IPM_script_19_20.R File 18 description: R code for IPM
 analyses between 2019-2020 #### METHODOLOGICAL INFORMATION A detailed
 description of data acquisition and processing can be found in the
 published manuscript in the Ecology (DOI). #### Code/Software All code can
 be generated in R with packages listed in files. Final figures were edited
 for publication in Affinity Designer.