Find research datasets worth reusing

openCC (other)Jan 2025View details →

Data from the Forest Resilience Threshold Experiment, University of Michigan Biological Station, 2024

During the 2024 field season, data collection efforts led by the FoRTE crew centered on understanding forest ecosystem dynamics and carbon cycling processes in a temperate forest landscape. Comprehensive datasets were gathered to evaluate structural and functional responses across multiple forest strata. Measurements included diameter at breast height (DBH) for canopy, subcanopy, and seedling layers, alongside a detailed subcanopy census to assess understory composition and diversity. Soil respiration (Rs) was monitored to quantify carbon fluxes, while fern density and distribution were documented to explore their role in forest microclimates and nutrient cycling. Photosynthetically active radiation (PAR) readings provided insights into light availability and its impact on primary production. Advanced remote sensing tools, including LiDAR and normalized difference vegetation index (NDVI), were employed to characterize canopy structure, vegetation health, and spatial heterogeneity. These diverse datasets collectively contribute to a robust framework for analyzing forest resilience, recovery, and carbon sequestration potential following disturbance, advancing our understanding of ecosystem processes in the face of environmental change.

openCC (other)Nov 2022View details →

Critical slowing down: vegetation height, cover and composition assess resilience of tidal fresh, brackish and salt marshes following experimental disturbance at twelve sites across the estuarine landscape

We investigated the relative recovery rates of vegetation cover, vegetation height and community composition over a period of >10 years after experimental disturbance in 12 tidal marshes (two oligohaline, three mesohaline and seven polyhaline sites) across the estuarine landscape on the coast of Georgia, USA. Vegetation was removed from three by three meter plots (four per site) with herbicide in 2006 followed by repeated clipping through 2009. Vegetation height, cover and composition was assessed in experimental and control plots (4 of each per site) from 2010 to 2020. The goal of the study was to assess how quickly the sites recovered from disturbance, how this varied in different types of marshes, and how results varied depending on whether recovery was assessed by vegetation height, cover or composition.

openCC (other)Nov 2023View details →

MCR LTER: Coral Reef: Modeling the effects of selectively fishing key functional groups of herbivores on coral resilience; data for Cook et al., 2023 Ecosphere

These data and code were generated in support of the manuscript: Cook DT, Schmitt RJ, Holbrook SJ, and HV Moeller, Ecosphere. To investigate the impacts of selectively harvesting functional groups of herbivorous fishes on coral resilience, we used a dynamic model that is grounded by the coral reef system in Moorea, French Polynesia. Our model simulates the fraction of a reef occupied through time by classes of key benthic spaceholders (coral, two stages of macroalgae, and turf). Benthic and fishing dynamics are linked through the harvesting of two functional groups of herbivorous fishes. We utilize data collected on the abundance of fishes on the reef and in the catch in Moorea, French Polynesia to inform our model and to empirically explore patterns of fishing selectivity. These data and code were published in Ecosphere and were a part of the thesis of D. Cook (2023). This manuscript uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2023).

openCC (other)Dec 2022View details →

Cascade Project at North Temperate Lakes LTER High Frequency Sonde Data from Food Web Resilience Experiment 2008 - 2011

High-frequency sonde data collected from the surface waters of two lakes in Upper Peninsula of Michigan during the summers of 2008-2011. The food web of Peter Lake was slowly transformed by gradual additions of Largemouth bass (Micropterus salmoides) while Paul Lake was an unmanipulated reference. Sonde data were used to calculate resilience indicators to evaluate the stability of the food web and to calculate ecosystem metabolism.

zenodo52/100

Resilience estimates of Amazon and Congo rainforests based on mean annual precipitation and root zone storage capacity

Resilience refers to the capacity of the ecosystem to absorb perturbations and remain in its native stable state. Here, we quantified forest resilience of South American and African ecosystems using mean annual precipitation and root zone storage capacity (2000-2019). We adopted Hirota et al. (2011) methodology for calculating resilience using logistic regression.  This logistic regression predicts the probability of forest (tree cover > 50%) as a function of the independent variable. The predicted resilience estimates range between 0 to 1, where 1 represents the highest probability of finding forest – interpreted as highly resilient forest ecosystems. For more information, check: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16115">https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16115</a>

opencc-by-4.0Jan 2022View details →

edi52/100

Monthly Spartina alterniflora marsh vegetation data for additional sites along the Georgia coast used in the Belowground Ecosystem Resiliency Model

Study plots (1-m2) were established in three Spartina alterniflora-dominated marshes - 2 on Sapelo Island, Georgia, and 1 on Skidaway Island, Georgia, and sampled once each during May, July, August, September, and October of 2016. Nine replicate plots were placed in vegetated marsh along transects that spanned 3 Landsat-8 pixel footprints, with 3 plots per pixel foot print. In each plot, measurements included plant biomass, plant species, stem density, and height. Aboveground biomass was calculated using allometric relationships between plant height, flowering status and mass from plant clipping studies. During these surveys, destructive core sampling was also performed in the proximity of the plots (n = 1 per plot) to measure above and below ground biomass. Chlorophyll, foliar N, and Leaf Area Index measurements were taken in the proximity of the plots.

openCC (other)Jul 2021View details →

edi52/100

MCR LTER: Coral Reef: Patterns and implications of spatial covariation in herbivore functions on resilience of coral reefs

These data and code were generated in support of the manuscript: Cook DT, Holbrook SJ, and Schmitt RJ, Scientific Reports. In 2017, we collected biological and physical data from 20 sites along the north shore of Moorea, French Polynesia, to investigate spatial patterns in grazing and browsing functions of herbivorous fishes, environmental correlates, and implications for coral resilience. In addition to the data collected at the 20 north shore sites, we conducted a 10-day field experiment to assess the relationship between browsing intensity and potential of reversing a coral-to-macroalgae shift. This material uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2025).

openCC (other)Jan 2025View details →

zenodo48/100

Resilience of pig producers in Europe

This dataset collected for the organic core POWER project to assess resilience capacities of organic pig prodcuers in Austria, Danemark, Italy, Sweden and Switzerland. These datasets have been anonymized.    The resilience farm data are all data that where observed at farm level, and contain farm characterisitcs, namely <table> <tbody> <tr> <td>variable</td> <td>description</td> <td>values</td> </tr> <tr> <td>farm id</td> <td>unique identifier of the farm</td> <td>characters, including country code based on ISO2</td> </tr> <tr> <td>breeding type</td> <td>type of pig entreprise on the found on the farm</td> <td>breeding, finishing or both</td> </tr> <tr> <td>entrerprise_x</td> <td>description of other entreprises found on the farm</td> <td>feed production, cash crop, chicken, sheep, dairy, beef, direct marketing, tourism, on-farm processing, horse housing.</td> </tr> <tr> <td>number non-pig entreprise</td> <td>number of entreprise describes</td> <td>integer</td> </tr> <tr> <td>structure</td> <td>type of pig housing structure</td> <td>permanent, temporary, both</td> </tr> <tr> <td>outdoor area</td> <td>type of oudoor access for pig</td> <td>concrete, shifting arable land, permanent pasture</td> </tr> <tr> <td>LSU</td> <td>livstock standard units computed following Eurostat standards</td> <td>numeric</td> </tr> <tr> <td>pig/ha</td> <td>intensity of production as LSU/UAApig</td> <td>numeric</td> </tr> <tr> <td>self-sufficiency</td> <td>percentage of pig feed produced on farm</td> <td>numeric</td> </tr> <tr> <td>UAA pig</td> <td>utilized agricultural area for the pig production</td> <td>numeric</td> </tr> <tr> <td>UAA total</td> <td>utilized agricultural area of the farm</td> <td>numeric</td> </tr> </tbody> </table>   The resilience data is the result of the interpretation of farmers' resilience narratives, which were interpreted been interpreted using the Meuwissen et al, 2019 farming systems framework. The data is in long fromat and represents a particular resilience capacity related to a specific shock. More particularly, the data contains the follwing information     <table> <tbody> <tr> <td>variables name</td> <td>description</td> <td>values</td> </tr> <tr> <td>farm id</td> <td>unique identifier of the farm</td> <td>characters, including country code based on ISO2</td> </tr> <tr> <td>country</td> <td>country code</td> <td>based on ISO2</td> </tr> <tr> <td>question related to shocks</td> <td>shocks to which the resilience narrative related to</td> <td>input cost, price, outbreak, climate, legislation, labour, general</td> </tr> <tr> <td>narratives (a= first, b=second)</td> <td>identifier of the narrative within a question</td> <td>a, b</td> </tr> <tr> <td>capacity</td> <td>resilience capacity following the Meuwissen et al (2019) framework</td> <td>robustness, adaptability, transformability, non-resilience</td> </tr> <tr> <td>resilience attribute type</td> <td>resilience attribute based on an expanded interpretation the Meuwissen et al (2019) framework (see paper)</td> <td> functional diversity, response diversity, modularity, tighness of feedback, social capital, attitude, system reserve (physical captial -inherent), system reserve (physical capital -use), system reserve (natural capital -inherent) system reserve (human capital - use) </td> </tr> <tr> <td>resilience attribute</td> <td>description of the attribute that led to the resilience attribute type classification</td> <td> ability to convert to cash crop, ability to offer good working conditions, ability to switch brand, access to financial services, access to technical solutions, adapted crops, adding finishing section, adjust feed production, adjust volume of pig production, adjusting paddock size to enable double fencing, advisory and veterinary services, believe in organic, brand building with social media, build temporary shelter, build up savings, by-product through partnership, capacity to access more land, change external feed, change feed ratio, conservable end product, create microclimates, create new brand, created a young farmer network, customer relation, decrease pig, decrease pig production, direct marketing, diverse farm, diverse sale channels, do something else, double fencing, efficiency, entrepreneurship, excess cereal production, exploring governance model as no successor, family labour, farmer owned value chain, fencing, financial lock-in, flexible infrastructure (enabling), flexible pig keeping system, forest system, good indoor infrastructure, good infrastructure, good relation to customers, governmental support, habit, has margin, home feed production, inadequate salary, increase cash crop, increase own work, increase own working time, independent feed ratio, indoor keeping, indoor production, innovator, innovator (one welfare) , insurance, margins, mechanisation, mobile mode of production, neighbor network, neighborhood early warning, neighborhood network, new cooling infrastructure, niche production, no competition, no fencing option, no own farm, land or infrastructure, no qualified staff required, offering jobs to young people, other livestock, part time worker, partnership with other farmers, producing more home grown feed, profit, reduced pig production, rely on sectoral organization, resistant breed, robust animals, robust breed, sectoral power, sectoral response, short term feed contracts, social media, soil health, split production on other farms, staffing agency (through advisory services), sufficient outdoor space, sufficient pasture, sufficient space, sufficient space (enabling), switch to indoor production, switch to other livestock, tiredness in the sector, Too big to fail, training, unique pig keeping system, up-to-date infrastructure, volunteer networks, wallow, work with nature </td> </tr> </tbody> </table>   To compute the resilience capacity score (Cscore) assign 0 to lack of resilience, 1 to robustness, 2 to adpababilty and 3 to transformability. If there is more than one narrative with a different capacity, the average score between mentionned capacities was taken. Use following R code in dplyr mydata<- ResilienceDataPreProcessed %>%   mutate(code = ifelse(capacity=="robustness", 1,ifelse(capacity=="adaptability",10,ifelse(capacity=="transformability",100,ifelse(capacity =="no resilience capacity",1000,ifelse(capacity=="no long term resilience capacity",10000,ifelse(capacity=="no short term resilience",1000,NA)))))))%>%   group_by(farm, question)%>%   summarise(Ccode=sum(code))%>%   mutate(Cscore=ifelse(Ccode==1|Ccode==2| Ccode==3, 1,ifelse(Ccode==20|Ccode==10|Ccode==111,2, ifelse(Ccode==100|Ccode==200,3, ifelse(Ccode==11|Ccode==21, 1.5,ifelse(Ccode==110|Ccode==120, 2.5,ifelse(Ccode==101,3,ifelse(Ccode==1000,0,ifelse(Ccode==10001|Ccode==1001,0.5,NA)) ))) ))))   Resilience questionnaire Farm number: Farm name or ID: Country:   System descriptors <table> <tbody> <tr> <td> Breeding or finishing (or both) </td> <td>          </td> </tr> <tr> <td> Indoor or outdoor (or a mix) </td> <td>   </td> </tr> <tr> <td> Organic or conventional </td> <td>    </td> </tr> <tr> <td> Number of years organic </td> <td>      </td> </tr> </tbody> </table>   1) Has your farm experienced significant challenges in the last 5 years? <table> <tbody> <tr> <td> Yes or no? </td> <td> Yes / No </td> </tr> <tr> <td> If "no", what factars (farm/external) created this resilience? </td> <td>         </td> </tr> <tr> <td> If "yes", please describe the 1st challenge </td> <td>         </td> </tr> <tr> <td> What was the impact on the farm (production, animal health/welfare, work load, work life quality etc)? </td> <td>           </td> </tr> <tr> <td> Did this change your management or farm structure subsequently (and how)? </td> <td>         </td> </tr> <tr> <td> If "yes", please describe a 2nd challenge </td> <td>         </td> </tr> <tr> <td> What was the impact on the farm (production, animal health/welfare, work load, work life quality etc)? </td> <td>           </td> </tr> <tr> <td> Did this change your management or farm structure subsequently (and how)? </td> <td>         </td> </tr> </tbody> </table>     2) In the future, how do you feel your pig system would cope with these challenges: a) Decreasing or negative margins due to increased feed or other input costs? <table> <tbody> <tr> <td> Very severely (e.g. bankruptcy) </td> <td>   </td> </tr> <tr> <td> Severely (e.g. closure of pig enterprise) </td> <td>   </td> </tr> <tr> <td> Strong impact (e.g. large reduction in production) </td> <td>   </td> </tr> <tr> <td> Short term impact (e.g. reduced production) </td> <td>   </td> </tr> <tr> <td> Little impact (e.g. change ration) </td> <td>   </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Why?   </td> <td>           </td> </tr> <tr> <td> How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) </td> <td>               </td> </tr> </tbody> </table>   b). Decreasing or negative margins due to reduced pig prices? <table> <tbody> <tr> <td> Very severely (e.g. bankruptcy) </td> <td>   </td> </tr> <tr> <td> Severely (e.g. closure of pig enterprise) </td> <td>   </td> </tr> <tr> <td> Strong impact (e.g. large reduction in production) </td> <td>   </td> </tr> <tr> <td> Short term impact (e.g. reduced production) </td> <td>   </td> </tr> <tr> <td> Little impact (e.g. change ration) </td> <td>   </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Why?   </td> <td>           </td> </tr> <tr> <td> How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) </td> <td>               </td> </tr> </tbody> </table>       c) Wide spread disease outbreak such as African Swine Fever <table> <tbody> <tr> <td> Very severely (e.g. bankruptcy) </td> <td>   </td> </tr> <tr> <td> Severely (e.g. closure of pig enterprise) </td> <td>   </td> </tr> <tr> <td> Strong impact (e.g. large reduction in production) </td> <td>   </td> </tr> <tr> <td> Short term impact (e.g. reduced production) </td> <td>   </td> </tr> <tr> <td> Little impact (e.g. change ration) </td> <td>   </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Why?   </td> <td>           </td> </tr> <tr> <td> How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) </td> <td>               </td> </tr> </tbody> </table>   d) Climate change impact, e.g. severe storms, flooding, drought, hot seasons <table> <tbody> <tr> <td> Very severely (e.g. bankruptcy) </td> <td>   </td> </tr> <tr> <td> Severely (e.g. closure of pig enterprise) </td> <td>   </td> </tr> <tr> <td> Strong impact (e.g. large reduction in production) </td> <td>   </td> </tr> <tr> <td> Short term impact (e.g. reduced production) </td> <td>   </td> </tr> <tr> <td> Little impact (e.g. change ration) </td> <td>   </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> Why?   </td> <td>           </td> </tr> <tr> <td> How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) </td> <td>                 </td> </tr> </tbody> </table>       e) Changing legislation impact, e.g. increased floor space allowance indoors, mandatory access to pasture, more land required (lower stocking densities to reduce nutrient loads from pasture systems or in general for the whole farm) <table> <tbody> <tr> <td> Very severely (e.g. bankruptcy) </td> <td>   </td> </tr> <tr> <td> Severely (e.g. closure of pig enterprise) </td> <td>   </td> </tr> <tr> <td> Strong impact (e.g. large reduction in production) </td> <td>   </td> </tr> <tr> <td> Short term impact (e.g. reduced production) </td> <td>   </td> </tr> <tr> <td> Little impact (e.g. change ration) </td> <td>   </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Why?   </td> <td>           </td> </tr> <tr> <td> How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) </td> <td>                 </td> </tr> </tbody> </table>   f) Shortage in qualified ‘work-mass’ (difficulties in recruiting qualified employees) <table> <tbody> <tr> <td> Very severely (e.g. bankruptcy) </td> <td>   </td> </tr> <tr> <td> Severely (e.g. closure of pig enterprise) </td> <td>   </td> </tr> <tr> <td> Strong impact (e.g. large reduction in production) </td> <td>   </td> </tr> <tr> <td> Short term impact (e.g. reduced production) </td> <td>   </td> </tr> <tr> <td> Little impact (e.g. change ration) </td> <td>   </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Why?   </td> <td>           </td> </tr> <tr> <td> How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) </td> <td>                 </td> </tr> </tbody> </table> 3) Any other comments on resilience of their system? 4) General comments/system description?

opencc-by-4.0Dec 2021View details →

openCC0Sep 2024View details →

Tree mortality in Forest and Biodiversity 2: a tree diversity experiment to understand the consequences of multiple dimensions of diversity and composition for long-term ecosystem function and resilience

The Forest and Biodiversity (FAB2) experiment uses native tree species in varying levels of species richness, phylogenetic diversity, and functional diversity planted in 100 m2 and 400 m2 plots at 1 m spacing, appropriate for testing long-term ecosystem consequences. FAB2 was designed and established in conjunction with a prior experiment (FAB1) in which the same set of twelve species was planted in 16 m2 plots at 0.5 m spacing. Both are adjacent to the BioDIV prairie-grassland diversity experiment, enabling comparative investigations of diversity and ecosystem function relationships between experimental grasslands and forests at different planting densities and plot sizes. This data package examines mortality in the first six years of the experiment.

openCC0Mar 2025View details →

FAB2_sapling_volume_2021-2022 in Forest and Biodiversity 2: a tree diversity experiment to understand the consequences of multiple dimensions of diversity and composition for long-term ecosystem function and resilience

openCC0Mar 2025View details →

fab2_allometry_2016-2022 in Forest and Biodiversity 2: a tree diversity experiment to understand the consequences of multiple dimensions of diversity and composition for long-term ecosystem function and resilience

openCC0May 2023View details →

CEE01 The Climate Extremes Experiment (CEE): Assessing ecosystem resistance and resilience to repeated climate extremes at Konza Prairie

Climate extremes, such as drought, are increasing in frequency and intensity, and the ecological consequences of these extreme events can be substantial and widespread. Yet, little is known about the factors that determine recovery (or resilience) of ecosystem function post-drought. Such knowledge is particularly important because post-drought recovery periods can be protracted depending on drought legacy effects (e.g., loss key plant populations, altered community structure and/or biogeochemical processes). These drought legacies may alter ecosystem function for many years post-drought and may impact future sensitivity (both resistance and resilience) to climate extremes. With forecasts of more frequent drought, there is an imperative to understand whether and how post-drought legacies will affect ecosystem response to future drought events. To address this knowledge gap, we experimentally imposed over an eight year period two extreme growing season droughts, each two years in duration followed by a two-year recovery period, in annually burned tallgrass prairie.

MCR LTER: Coral Reef Resilience: North Shore Herbivorous Fish Counts, Habitat Associations, and Substrate, 2010

These data describe the species abundance, size distributions, and habitat associations of roving herbivorous fishes (fishes belonging to the families, Acanthuridae, Scaridae, and Siganidae) found in different habitats in the lagoon and forereef on the north shore of Moorea. Adult fishes and large juveniles were counted (and their size estimated) by a SCUBA diver or snorkeler on thirty-four 50 m by 5 m wide transects. After counting large fishes, the entire transect was swam a second time, with the diver looking exclusively for small juvenile fishes on a 1 m swath. In addition to recording the species identity and estimated size of each juvenile encountered, the diver also recorded the particular microhabitat each individual or group of individuals was associated with. Finally, to quantify the relative availability of different types of microhabitat, the diver conducted point contacts where the primary benthic substrate was identified at regular (1 m) intervals on the same transect. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.

MCR LTER: Coral Reef Resilience: Herbivore Bite Rates on the North Shore Forereef July-August 2010

These data describe bite rates of two abundant species of roving herbivorous fishes found on the forereef, Chlorurus sordidus (Scaridae), and Ctenochaetus striatus (Acanthuridae). During several days in July and August 2010, a SCUBA diver followed individual focal fish for a period of up to 5 minutes and recorded the number of bites taken as well the types of substrates bitten. Upon randomly locating a focal individual, divers estimated the total length of that individual as well as their depth at the initiation (and termination) of the observation. Data are organized in two data tables. The first data table (Focal_Herbivore_Bite_Rates) contains individuals that were observed at two depths (~ 10 m and ~ 17 m) at LTER 1 as part of a balanced sampling design (see Sampling Protocol/Design). The second data table (Additional_Bite_Rate_Data) contains individuals that were observed opportunistically at LTER 1 and Resilience 2. To minimize the effect of time of day on fish behavior, all data were collected between 10:00 and 16:00, a period of time which corresponds with peak feeding rates for many herbivorous fishes. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.

MCR LTER: Coral Reef Resilience: Short-term Accumulation of Algal Biomass on Unglazed Ceramic Tiles from July 2010-August 2011

Caged tiles were placed at various sites to measure short-term algal accumulation in the absence of grazing by fishes or large invertebrates. These data document biomass of algae that accumulated on unglazed ceramic tiles (2.5 cm X 2.5 cm) placed inside cages (mesh size = 2.5 cm x 2.5 cm) at various sites around the island of Moorea for a period of 3 to 4 weeks. Three separate experiments were conducted (one during July-August 2010 and two in July-August 2011). In the first experiment (2010_Production), we measured the accumulation of algae after 24 days at two different depths on the forereef (10 and 17 m), and within the lagoon at four different distances from the reef crest (approximately 25, 100, 400, and 700 m). In the second experiment (2011_Production_Summary), we measured the accumulation of algae after 24 or 25 days at six forereef sites (LTER 1, Resilience 2, LTER 3, LTER 4, LTER 5, LTER 6). In this experiment, tiles were placed adjacent to the fish transects (see knb-lter-mcr.6) at a depth of 12 m. In the final experiment (Production_Time_Series), we measured the accumulation of algae on tiles at three day intervals over a period of 31 days. This experiment was conducted at Resilience 2. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.

MCR LTER: Coral Reef Resilience: Algae, Coral, and Sediment data from Grazing Intensity Experiment, 2010 - 2012

These data describe the percent cover of benthic space holders (primarily algae), the biomass of algae and sediment, and the number of corals recruiting on 15 cm X 15 cm terra cota tiles experimentally manipulated on the forereef on the north shore of Moorea. The experiment was established to test whether and how different levels of grazing influence benthic community development. Five treatments were initially established to create a gradient in grazing pressure with a sixth treatment established shortly thereafter. Each treatment was replicated ten times using a randomized block design. Each cage initially contained four tiles, and one tile from each cage has been photographed and destructively sampled for biomass at regular intervals. In addition to the original tiles deployed in July 2010, tiles were subsequently deployed in March 2011, August 2011, and March 2012 to test whether community development varies among seasons. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.

MCR LTER: Coral Reef Resilience: Benthic Dynamics on 5m by 5m Plots on the Forereef, 2009 - 2011

These data describe the dynamics of corals, invertebrates, and fishes on 5 m X 5 m plots on the forereef following an outbreak of corallivorous crown-of-thorns seastars (Acanthaster planci) that caused mass coral mortality (see Adam et al. 2011). Twenty plots were first established at Resilience 2 during July 2009. Half of these plots were randomly assigned to a structure removal treatment and all dead coral structure was removed by divers, while the other half remained unmanipulated. Following the establishment of these plots, in February 2010 this site was impacted strongly by tropical cyclone Oli with the result that most of the structure was removed from all plots. Consequently, during July 2010, a structure/no structure experiment was initiated on ten plots (5 removals and 5 controls) at a site not impacted by Cyclone Oli (Resilience 3). Finally, during July 2011, five unmanipulated plots were established at each of four additional sites (Resilience 1, 4, 5, 6) so that community trajectories could be compared among sites. Counts of fishes, invertebrates, and corals are made regularily (at least annually). Coral and invertebrate counts are each separated into two data tables to reflect slightly different methodology. Initial coral counts included counts (and size estimates) of all stony corals, while later counts focused on corals from three genera, Pocillopora, Acropora, and Porites. Initial counts of invertebrates did not include information on size; all invertebrates were measured in later counts. In addition to in situ counts, all plots are photographed annually in 0.5 m by 0.5 m segments (e.g., 100 photos per plot). This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la Rép

MCR LTER: Coral Reef Resilience: Live and Dead Pocillopora and Acropora Coral Colony Time Series from 2006 to 2011

These data describe the abundance, size structure, and morphologies of living and dead corals belonging to the genera Pocillopora and Acropora on the forereef (depth = 10 meters) in 2006, 2009, 2010, and 2011. Data were derived from a randomly chosen subset of photo quadrats associated with knb-lter-mcr.4. For each quadrat, individual coral colonies were identified to genus, scored as living or dead, and the total area of their footprint calculated. In addition, branch morphology was scored on a scale from 1 to 3, with 1 representing very tight spacing, and 3 representing open spacing among adjacent branches. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.