Describe the role of potential sponsors or funders in study design, data collection and analysis, decision to publish, or manuscript preparation. If yes, please provide details of any and all competing interests in the box below. If no author has any competing interests to declare, enter this statement in the box: "The authors have declared no competing interests.".
All information entered here should also be included in the methods section of your manuscript. Please enter the name of the IRB or ethics committee that approved this study in the space below. Rising temperatures and deteriorating water quality are common indicators of ocean change 73 .
Optical remote sensing has been a key tool in quantifying changes in the oceans on a global and 85. Long-term in situ dataset documenting temporal changes in the coastal zone environment where 98.
Materials and methods
CARICOMP Dataset
CARICOMP data are available in the Supporting Information (a description of all stations is in Tables 139. S1-A and S1-B, and data in Appendix S2). Simple mixed-effects models to estimate differences between habitats (fixed factor) including all 144 .
Global and local-scale changes across the Caribbean
Correlates of global and local-scale changes
Wind-driven wave exposure for each station depends on wind patterns and the configuration of 216. From these data sets wave exposure was calculated using methods based on wave theory described in 222. Granular density data of human population for the years 1990 and 2000 (the most recent dataset available at 233 . that spatial detail) was obtained from the Global Rural-Urban Mapping Project, Version 1 [GRUMPv1:.
As input data, we used adjusted population density grids that provide population density in 237. persons per square kilometer using census data, as well as observations of night lights to 238. delineate the extent of urban areas. From these data sets, we extracted the number of people inside a buffer with a diameter of 1-239. degrees around each station and then calculated the population difference between 2000 and 1990, which provides a proxy for the broad impacts of human population expansion on coastal 241. Satellite precipitation data were obtained from the combined GPCP precipitation dataset v2.2, which combines 245 .satellite and gauge precipitation values in monthly estimates of total precipitation from 1986 to 246,246.
When trends are insignificant, their value is uninformative (e.g. a temperature trend of 2 °C year-1 251 .. with a p value of 0.8 is meaningless), which hinders the use of actual trend values as response variables 252. We therefore transformed continuous data ( i.e. trend values in temperature 253 .. and visibility) into nominal data (i.e. trend categories) by classifying trends as non-significant, 254 .
Results
Data collected by the CARICOMP network provided evidence of widespread local but not global distribution 304. Of the 28 reef, seagrass and mangrove stations, 18% (1 mangrove, 2 seagrass meadow and 2 317 . stations of coral reefs) showed a significant trend of increasing temperature, and only one (Bonaire reef) 318. The presence of negative, positive or non-significant temperature trends was not explained by 326.
Predicted probability of decrease and increase in visibility (angle 341 . to right labels in upper and lower panels, respectively) given changes in human population 342 .
Discussion
Site-specific information about the inherent characteristics of the time series can be used to help 372. Within this data set, given the variability and memory of the time series, Puerto Morelos in 378. This information can be used to set realistic expectations for trend detectability at different locations.
The Caribbean basin is becoming drier [59] due to the intensification of the Caribbean Low Level Jet 410. Knowledge of the factors that modulate the detection of visibility trends can also assist the 413. Chronic declines in coastal water quality can are associated with the increase in sea diseases [62] and the 418.
For example, the CARICOMP data may be useful in identifying and assessing indicators of the long-term impacts of marine protected areas 439. Tussenbroek et al [72] and the documentation of changes in mangrove and reef communities are 450.
Acknowledgements
Effects of terrestrial runoff on the ecology of corals and coral reefs: review and synthesis.
Supporting information
That is, the residuals at time t are a function of the residuals at time t-1 (i.e. .. the temporal "memory" of the time series has a one month lag), depending on the station-specific 193. Sixty percent of the CARICOMP stations (described in Table I) included long-term records and were 312. Site-specific information about the inherent characteristics of the time series can be used to help the 390 .
For example, the CARICOMP data may be useful in identifying and assessing indicators of the long-term impacts of marine protected areas 462. MPA. All these adjustments strengthened the manuscript, but did not alter the analyzes or results, nor did they change the message of the article. That is, the residuals at time t are a function of the residuals at time t-1 (i.e. the temporal 'memory' of the time series has a one-month lag), depending on the station-specific autocorrelation parameter , together with the noise (ϵt):”.
For example, CARICOMP data can be useful in identifying and assessing indicators of the long-term effects of marine protected areas (MPAs) by comparing areas outside internal MPAs [e.g. Site-specific information on the inherent characteristics of the time series can be used to help identify monitoring sites that are cost-effective in terms of being able to detect trends earlier [27] if detecting early changes is the main goal. This means that the residuals at time t are a function of the residuals at time t-1 (i.e. the time .. memory" of the time series has a one-month lag), depending on the station-specific autocorrelation parameter ϕ together with noise [ϵt, 27]: ".
For example, CARICOMP data can be useful in identifying and assessing indicators of the long-term effects of marine protected areas (MPAs). That is, the residuals at time t are a function of the residuals at time t-1 (i.e., the temporal "memory" of the time series has a one-month lag), depending on the station-specific autocorrelation parameter ϕ together with the noise (ϵt, 27): .
