Temporal and spatial variability in coral recruitment on two Indonesian coral reefs: consistently lower recruitment to a degraded reef

Salinas‐de‐León, Pelayo; Dryden, Charlie; Smith, David J.; Bell, James J.

doi:10.1007/s00227-012-2066-7

Cited by 41 publications

(38 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Coral recruitments have been intensively studied in different geographic regions during the past three decades (e.g., Birkeland 1977;Harriott 1992;Hughes et al 1999;Soong et al 2003;Glassom et al 2004;Nozawa et al 2006;Adjeroud et al 2007;Salinas-de-León et al 2013). These studies have pointed out that recruitment rates are highly variable on spatial and temporal scales.…”

Section: Introductionmentioning

confidence: 99%

Coral recruitment of a subtropical coral community at Yenliao Bay, northern Taiwan

Dai

2014

Zool. Stud.

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Coral recruitment of a subtropical coral community at Yenliao Bay, northern Taiwan

Dai

2014

Zool. Stud.

View full text Add to dashboard Cite

show abstract

“…In contrast to the findings in our study, previous authors recorded no differences in coral recruitment between natural reef substrate and artificial settlement substrates (Salinas-deLeón et al, 2011). Although there are no clear reports for coral spawning times in Spermonde (Sawall et al, 2013), strong indications are that it occurs between February and April (Salinas-de-León et al, 2013;Yusuf et al, 2013), thus slightly before our sampling period started. Larvae from this spawning period, especially mass-spawning species, would have already settled onto the reef, with a lower abundance of mass-spawned larvae in the surrounding waters during tile deployment.…”

Section: Coral Recruitmentmentioning

confidence: 90%

“…Compared to this first phase of recruitment, numbers of coral recruits on the tiles decreased afterwards, with the exception of one of the frames where it remained constant. This could indicate a higher impact of the full moon or a spawning event at the beginning of the sampling period (Salinas-de-León et al, 2013;Yusuf et al, 2013) at the near-shore site as well. However, since coral recruits at that site also mostly comprised pocilloporids, other factors such as currents or post-settlement mortalities are more likely to have played a role in this temporal settlement pattern, but could not be investigated during our study.…”

Section: Coral Recruitmentmentioning

confidence: 99%

Bacterial Biofilm Communities and Coral Larvae Settlement at Different Levels of Anthropogenic Impact in the Spermonde Archipelago, Indonesia

Kegler

Gärdes

et al. 2017

Front. Mar. Sci.

View full text Add to dashboard Cite

Populations on small islands surrounded by coral reefs often heavily depend on the services provided by these reefs. The health and recovery of reefs are strongly influenced by recruitment of coral larvae. Their settlement relies on cues such as those emitted from bacterial communities forming biofilms on reef surfaces. Environmental conditions can change these bacterial community compositions (BCC) and may in turn affect settlement of coral larvae. At three small inhabited islands in the Spermonde Archipelago, Indonesia, with different distance from the mainland, BCC and coral recruitment were investigated on artificial ceramic tiles after 2-8 weeks exposure time and on natural reef substrate. Water parameters showed a clear separation between inshore and near-shore/mid-shelf sites, with distinct benthic communities at all three sites. No coral recruitment was observed at the inshore site with highest natural and anthropogenic stressors. At the other two sites coral recruitment occurred on natural surfaces (recruits per 100 cm 2 : 0.73 ± 1.75 near-shore, 0.90 ± 1.97 mid-shelf), but there was no significant difference between the two sites. On artificial substrates coral recruitment differed between these two sites, with tile orientation and with exposure time of the tiles in the reef. The most abundant bacteria on both substrates were Gammaproteobacteria, Alphaproteobacteria, and Cyanobacteria. BCC was strongly correlated with water quality and significant differences in BCC between the inshore site and near-shore/mid-shelf were found. On artificial substrates there was a significant difference in BCC also with exposure time in the reef. Our study highlights the value of taking both BCC and coral recruitment into account, in addition to the environmental conditions, when considering the recovery potential of coral reefs.

show abstract

“…, , Salinas‐de‐Leon et al. ). However, these pressures were present throughout the period of this study and prior to it, and so the reef had most likely already responded to these pressures, i.e., if there was a regime shift at this site, it occurred prior to 2007.…”

Section: Discussionmentioning

confidence: 99%

Detecting ecological regime shifts from transect data

Ward

Wotherspoon

Melbourne-Thomas

et al. 2018

Ecological Monographs

View full text Add to dashboard Cite

Timely detection of ecological regime shifts is a key problem for ecosystem managers, because changed ecosystem dynamics and function will usually necessitate a change in management strategies. However, currently available methods for detecting regime shifts depend on having multiple long time series data from both before and after the regime shift. This data requirement is prohibitive for many ecosystems. Here, we present a new approach for detecting regime shifts from one‐dimensional spatial (transect) data from just a single time step either side of the transition. Characteristic length scale (CLS) estimation is a method of attractor reconstruction combined with nonlinear prediction that enables identification of the emergent scale at which deterministic behavior of the system is best observed. Importantly, previous studies show that a fundamental change in ecosystem dynamics, from one domain of attraction to another, is reflected in a change in the CLS, i.e., the approach enables distinguishing regime shifts from variability in dynamics around a single attractor. Until now the method required highly resolved two‐dimensional spatial data, but here we adapted the approach so that the CLS can be estimated from one‐dimensional transect data. We demonstrate its successful application to both model and real ecosystem data. In our model test cases, we detected change in the CLS in cases where the shape (topology) of the interaction network had changed, leading to a shift in community composition. In an examination of benthic transect data from four Indonesian coral reefs, changes in the CLS for two of the reefs indicate a regime shift. This new development in estimating CLSs makes it possible to detect regime shifts in systems where data are limited, removing ambiguity in the interpretation of community change.

show abstract

Temporal and spatial variability in coral recruitment on two Indonesian coral reefs: consistently lower recruitment to a degraded reef

Cited by 41 publications

References 49 publications

Coral recruitment of a subtropical coral community at Yenliao Bay, northern Taiwan

Coral recruitment of a subtropical coral community at Yenliao Bay, northern Taiwan

Bacterial Biofilm Communities and Coral Larvae Settlement at Different Levels of Anthropogenic Impact in the Spermonde Archipelago, Indonesia

Detecting ecological regime shifts from transect data

Contact Info

Product

Resources

About