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Seamounts
hotspots of pelagic biodiversity
in the open ocean
Telmo Morato, S. Hoyle, V. Allain, S. Nicol
I.S.R Portugal, IMAR-Açores Portugal, Secretariat of the Pacific Community New Caledonia
Seamounts – Definition
It has been defined many times (1-6) but there is no
“generally accepted” definition;
Before (1,5): undersea mountains that rise more than
1000m from the sea floor to below sea level;
SBN definition (6,7): “any geographically isolated
topographic feature on the seafloor taller than 100 m,
including ones whose summit regions may temporarily
emerge above sea level”
1- Menard, 1964; 2- Wessel, 2001; 3- Schmidt and Schmincke, 2000; 4- Pitcher et al., 2007; 5- International Hydrographic Organization, 2008; 6Wessel et al., 2010; 7- Staudigel et al. 2010
Seamounts – How many are out there?
“the surfaces of Mars, Venus, and the Moon are
much better mapped than Earth’s ocean floors”
Sandwell et al., 2002
Seamounts – How many are out there?
Pitcher et al., 2007
Seamounts – How many are out there?
Our estimates for the whole world
Locations for 14,287 seamounts. Yellow indicates seamounts within EEZs (59% of total), blue indicates
seamounts in international waters (41%).
Data by Kitchingman et al., 2007
Seamounts – How many are out there?
Height-frequency distributions of seamounts. There could be approximately 125,000 seamounts taller
than 1 km, and as many as 25 million taller than 100 m, but the uncertainties are considerable. The white
star (3 million) is an adjusted prediction by hillier and Watts (2007).
Wessel, 2010
Rationale– Biodiversity hotspots
There has been considerable debate about the status and
sustainability of pelagic fisheries around the world (1–4)
It has been demonstrated that many species may be protected
by identifying biodiversity hotspots and managing them for
conservation (5).
This approach is well established for terrestrial systems and
marine tropical reefs (6, 7), but less so for the pelagic ecosystems
of the open ocean (5).
1- Baum et al., 2003; 2- Myers and Worm, 2003; 3- Hampton et al., 2005; 4- Hilborn, 2006; 5- Worm et al., 2003; 6- Myers et al., 2000; 7Roberts et al. 2002
Rationale– Biodiversity hotspots
Hotspots that have been identified in open ocean areas have
been typically associated with particular environmental factors
and mesoscale oceanographic features such as fronts or eddies
(1, 2);
To address this issue, dynamic marine reserves that move with
the wildlife have been suggested (3), but such approaches may
not be workable (4).
1- Worm et al., 2005; 2- Etnoyer et al., 2004; 3- Norse, 2006; 4- Malakoff, 2004
Objectives– Seamounts are hotspots?
We examined if seamounts aggregate pelagic biodiversity by applying ocean
basin scale generalized linear models (GLMs) to location-specific fisheries catch
data.
In addition, we analyzed catch per unit of effort (CPUE) in relation to distance to
seamounts to identify those pelagic species that are significantly associated
with seamounts.
Science, 27 May 2010
Morato et al. 2010
Methods– Seamount Database
Seamounts included in the present study (n=1145)
Morato et al. 2010
Methods– Observer Database
Location of the 24338 longline sets monitored by the observer program (1980 -2007)
Morato et al. 2010
Methods– Observer Database
Total longline fishing effort (number of hooks; 1980-2007.) Each cell has 50x50 km.
Morato et al. 2010
Methods– Observer Database
Mean catch per unit of effort (CPUE in number of individuals per 100 hooks) of by-catch
species in the tuna longline (1980-2007). Albacore, bigeye, skipjack and yellowfin tuna
species are not included in this figure
Morato et al. 2010
Methods– Statistical analyses
Pelagic Biodiversity analyses
The expected number of species (Ŝ40), standardized to 1000 hooks per longline
set, was rarefied for subsamples of 40 individuals
The effects of habitat type and distance to habitat feature were analyzed for the
estimated rarefied richness.
GLM techniques were used to standardize rarefied richness and to evaluate
whether the presence of habitat features and the distance to the feature were
significant explanatory variables.
The explanatory variables included in the model were year, moon phase,
geographical area, fleet type, distance to the closest feature, and fishing effort.
The Akaike's Information Criterion (AIC) was used to compare the model fits
Morato et al. 2010
Results – Rarefied Diversity
Mean expected species diversity (±95% confidence limits) rarefied from 40 individuals
(Ŝ40) as a function of: (A) the main habitat (seamount -SM-, shore and oceanic) where all
means are significantly different at =0.01 (ANOVA and Tukey's Honestly Significant
Difference Test), (B) distance to seamount summit where the fitted logarithmic
regression is also shown (grey line), and (C) 5 degrees latitude
Morato et al. 2010
Results – Rarefied Diversity
Expected species diversity rarefied from 40 (Ŝ40) individuals as a function of 1x1 degree
cells. Stars denote locations of seamounts with longline sets close to their summits.
Morato et al. 2010
Results – Rarefied Diversity
55,394
Summary statistics for the GLM single variable elimination analyses relating species
diversity with habitat and other variables. AIC is the Akaike's Information Criterion.
The relationship for describing species diversity was complex with distance to features, number of hooks and
latitude the strongest predictors of species diversity
Morato et al. 2010
Results – Rarefied Diversity
The effect of the variables distance*feature on species diversity rarefied from 40
individuals (Ŝ40). One variable was predicted at a time from the results of the GLM by
fixing the other variables.
When all variables except distance to feature were kept constant, seamounts were found to have higher rarefied
diversity within 30-40 km of the summit.
Morato et al. 2010
Results – By-catch species
Effect of including the term for distance to seamount on the AIC (ΔAIC), the parameter
estimate for the relationship with log(distance to seamount), and whether the effect
represents a significantly higher or lower catch rate close to seamounts (SM). Only those taxa
with statistically significant trends are shown here.
There was sufficient data to
analyze 37 taxa
seamount aggregation
effects were detected for
41% of the taxa
(15 taxa of shark, billfish
and pelagic teleost fish)
the opposite were detected
for only three taxa
Morato et al. 2010
Results – By-catch species
Analyses of the GLMs for each highly migratory pelagic species
For the shark taxa the probability of catching the species / or average
number caught increased closer to seamounts for:
porbeagle shark (Lamna nasus)
short-finned mako shark (Isurus oxyrinchus)
silky shark (Carcharhinus falciformis)
silky sharks
blue shark (Prionace glauca)
decreased for pelagic:
stingray (Pteroplatytrygon violacea)
Visitors to a seamount –
Blue Planet A Natural History of the Oceans - BBC
Morato et al. 2010
Results – By-catch species
Analyses of the GLMs for each highly migratory pelagic species
For the billfishes and tunas taxa the probability of catching the species /
or average number caught increased closer to seamounts for:
yellowfin tuna (Thunnus albacares)
blue marlin (Makaira nigricans)
swordfish (Xiphias gladius)
and decreased for:
albacore (Thunnus alalunga)
shortbill spearfish (Tetrapturus angustirostris)
Visitors to a seamount –
Blue Planet A Natural History of the Oceans - BBC
Morato et al. 2010
Results – By-catch species
Analyses of the GLMs for each highly migratory pelagic species
For the other pelagic teleost fish taxa the probability of catching the
species / or average number caught increased closer to seamounts for:
ribbon fish (Trachipterus trachypterus)
butterfly kingfish (Gasterochisma melampus)
big-scaled pomfret (Taractichthys longipinnis)
Atlantic pomfret (Brama brama)
long-snouted lancetfish (Alepisaurus ferox)
short-snouted lancetfish (Alepisaurus brevirostris)
moonfish (Lampris guttatus)
Visitors to a seamount –
Blue Planet A Natural History of the Oceans - BBC
Morato et al. 2010
Conclusions – Pelagic biodiversity analyses
1. Our analyses suggest that seamounts are hotspots of pelagic biodiversity, since
they show consistently higher species richness than do shore or oceanic areas.
2. Moreover, our study indicates that higher species diversity is likely to occur within
30 to 40km of seamount summits.
3. This study also demonstrates that many marine predators and other visitors are
associated with seamounts.
4. Conserving biodiversity hotspots has been demonstrated to yield significant
conservation benefits
5. Therefore, our analyses support the utility of seamounts as potential locations for
offshore marine reserves.
6. Seamount habitats are easier to conserve than ephemeral areas since they are
easier to map, survey and enforce.
7. The establishment of a network of marine reserves on seamounts may help to
conserve pelagic biodiversity and achieve sustainability of marine predator
species
Morato et al. 2010
Thanks
We wish to acknowledge the SPC member countries for the
collection and provision of observer data.
Nick Davies and Michael Manning for help with the modeling;
Emmanuel Schneiter, Colin Millar and Peter Williams for helping
with the databases