Aligning marine species range data to better serve science and conservation

<div><p>Species distribution data provide the foundation for a wide range of ecological research studies and conservation management decisions. Two major efforts to provide marine species distributions at a global scale are the International Union for Conservation of Nature (IUCN), which provides expert-generated range maps that outline the complete extent of a species' distribution; and AquaMaps, which provides model-generated species distribution maps that predict areas occupied by the species. Together these databases represent 24,586 species (93.1% within AquaMaps, 16.4% within IUCN), with only 2,330 shared species. Differences in intent and methodology can result in very different predictions of species distributions, which bear important implications for scientists and decision makers who rely upon these datasets when conducting research or informing conservation policy and management actions. Comparing distributions for the small subset of species with maps in both datasets, we found that AquaMaps and IUCN range maps show strong agreement for many well-studied species, but our analysis highlights several key examples in which introduced errors drive differences in predicted species ranges. In particular, we find that IUCN maps greatly overpredict coral presence into unsuitably deep waters, and we show that some AquaMaps computer-generated default maps (only 5.7% of which have been reviewed by experts) can produce odd discontinuities at the extremes of a species’ predicted range. We illustrate the scientific and management implications of these tradeoffs by repeating a global analysis of gaps in coverage of marine protected areas, and find significantly different results depending on how the two datasets are used. By highlighting tradeoffs between the two datasets, we hope to encourage increased collaboration between taxa experts and large scale species distribution modeling efforts to further improve these foundational datasets, helping to better inform science and policy recommendations around understanding, managing, and protecting marine biodiversity.</p></div

Effect of FAO Major Fishing Area constraints on AquaMaps distributions.

<p>(A) AquaMaps species distribution of <i>Hoplichthys regani</i>, the ghost flathead, with known occurrence records. (B) Aggregated AquaMaps predicted ranges for 3,208 species whose equatorial distribution encounters an eastern discontinuity exactly at 175° W, the boundary between FAO Major Fishing Areas 71 and 77 (shown in blue). Other FAO area boundaries create additional clear discontinuities.</p

Comparison of alignment between AquaMaps and IUCN range data.

<p>(A) Distribution alignment (overlap of smaller range within larger) versus area ratio (the ratio of smaller range area to the larger range area) for 2,330 species included in both IUCN and AquaMaps datasets. The upper right quadrant comprises species whose maps largely agree in both spatial distribution and the extent of described ranges (n = 522; 22.4% of paired map species). The upper left quadrant comprises species whose maps agree well in distribution, but disagree in area (n = 715; 30.7%). The lower right quadrant includes species for which the paired maps generally agree in range area, but disagree on where those ranges occur (n = 649; 27.9%). The lower left quadrant indicates species for which the map pairs agree poorly in both area and distribution (n = 444; 19.1%). (B) Alignment quadrant breakdown of species by taxonomic group.</p

Taxonomic and geographic coverage of AquaMaps and IUCN range data.

<p>(A) Number and proportion of species by taxa included in each dataset (22,889 species in AquaMaps, 4,027 species in IUCN). Overlapping species are dominated by bony fishes (994 species, primarily tropical taxa) and corals (394 species). (B, C) Global marine species count per 0.5° cell according to (B) AquaMaps and (C) IUCN. The margin frequency plots show relative species count per cell at each latitude and longitude.</p

MPA gap analysis results based upon alternate choices of datasets.

<p>Percent of species range covered by MPAs based upon methods in Klein et al. (2015). Scenario 1 replicates the original results, measuring protected range of species in AquaMaps version 08/2013 dataset, with a 50% presence threshold, against the 2014 World Database of Protected Areas, filtered for IUCN categories I-IV that overlap marine areas. Scenario 2 updates the results using AquaMaps version 08/2015, showing very small changes despite the inclusion of an additional 5,545 species. Scenario 3, still using 2015 AquaMaps data, drops the presence threshold to zero, showing an expected decrease in gap species, but also a decrease in species with 5% or greater protected range. Scenario 4 examines species MPA coverage using only the IUCN dataset.</p

Summary statistics and ancillary data of published single driver-response relationships in pelagic marine ecosystems

We created a database of published single driver-response relationships in marine pelagic ecosystems that were deemed significant based on p values ≤ 0.05 or were included in best-fit models identified through model selection. Multiple summary statistics were recorded (when available) in the database in an effort to explore variation in driver-response relationships in the present study and to be made available for researchers for future studies. The summary statistics include published or derived shapes of the relationships (linear, non-linear or specific functional forms), sample size, quantitative estimates of ecological thresholds, p values, R^2, deviance explained, correlation and regression coefficients, and model covariates (if multivariate model). In addition, we collected ancillary data on study characteristics to explore the variation in driver-response relationships and to identify the most robust papers with respect to statistical methods. The ancillary data in our database include ecosystem type (enclosed bay or sea, coastal pelagic, continental shelf and continental slope/oceanic), local region, ocean basin, large marine ecosystem, temporal scale of study, functional level (i.e., individual, population, community) and species trophic level (TL 1-4) of ecological response, primary productivity (mgC/mg2/day) and the statistical methods used by the authors. Estimates of species trophic level and primary productivity were obtained from the Sea Around Us Project (http://www.seaaroundus.org/). See Supplement Table S1 for additional description of data columns. The references in the database are cross-referenced with Table S1 Literature Cited.docx

Ten public goals and sub-goals showing benefit measured under each.

<p>Ten public goals and sub-goals showing benefit measured under each.</p

Goal and sub-goal scores for Brazil regional analysis (left), and Brazil global analysis (right).

<p>Key differences are found in Artisanal Fishing Opportunities, Tourism and Recreation, Lasting Special Places and Iconic Species. Overall Index scores (center) for the regional study are remarkably similar to global results for Brazil.</p

Current and likely future status for each state's overall Index score (axis values) and the value from an independent measure of development status (IFDM) used in Brazil (size of data point).

<p>Points below the dashed line are trending negatively into the future, and above are trending positively. IFDM scores range from 0 to 1 (low development =  0–0.4, average development  =  0.4–0.6, moderate development =  0.6–0.8, and high development =  0.8–1).</p

Overall Index, goal and sub-goal scores for Brazil (country) and each Brazilian coastal state.

<p>Empty cells are goals not relevant to that region. Goals (two-letter codes) and sub-goals (three-letter codes) are reported separately; LE, SP and BD goals are the average of sub-goal scores; FP scores are the weighted average of sub-goal scores. Acronyms are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092589#pone-0092589-t001" target="_blank">Table 1</a>.</p