Taxonomic Composition
The PCoA shows that the majority of taxonomic variation can be explained
by one axis (68.8% for field data, 52.0% for the full OBIS dataset:
Fig. 3). Latitudinally, this axis represents a broad northern and
southern cluster with a transition in between (Fig. 3). This
biogeographic pattern is present regardless of data subsetting, but
clustering is more apparent in the field data than in any OBIS dataset.
This is consistent with results based on family level data (Fig. S3).
When comparing family-level changes in the field data, northern sites
have higher proportions of Cardiidae, Psammobiidae, Tellinidae, and
Veneridae (Fig. 4). Southern sites are represented by higher proportions
of Carditidae and of smaller families (Fig. 4).
When biogeographic variables were compared to environmental variables in
a series of spatial autoregressions, temperature was consistently a
significant predictor of biogeographic structure across all datasets
(Table S3).
Discussion
A strong latitudinal diversity pattern can be seen for marine bivalves
along the eastern coastline of Australia. This pattern is consistent
regardless of which diversity estimators are used and whether field
sites are subdivided along a midpoint.
Strong latitudinal gradients were found regardless of the diversity
metric used. Depending on the method used, however, the intensity of the
latitudinal gradient changed significantly. Estimated richness values
(S2/m) were the most compelling in the field data,
which showed weaker gradients using other metrics. OBIS data showed a
consistent gradient using all methods but failed to show consistency
when the dataset was reduced to measure richness at a local scale,
presumably due to insufficient sample sizes.
Including observational data in the OBIS datasets resulted in the
largest shift in gradient strength. Observational records made up less
than 20% of the data, but their inclusion massively reduced the
latitudinal diversity signal across all the analytical treatments (Fig.
2, Table 1). Previous studies have shown large discrepancies in sampling
effort across space (Brown et al. , 2000, Pressey, 2004), with a
higher degree of observations often resulting in apparent undersampling
(Geldmann et al. , 2016). Observational values are typically
included in global studies, where they are often subject to screening
based on species ecology (Gagné et al. , 2020), but as shown here,
they may present issues at the regional scale. Compiling citizen science
information presents a similar challenge to that of using composite
datasets: the sampling method often highly varies (Pocock et al. ,
2017) and very few schemes operate at scales large enough to measure
latitudinal patterns – especially for invertebrate groups.
Despite inconsistencies in sampling effort and methods present in large
datasets such as OBIS, they do suggest latitudinal diversity patterns in
marine bivalves that are broadly consistent with those demonstrated by
field collections arrayed at a regional scale. This fact indicates that
OBIS data, previously used in many global studies for marine diversity
and biogeographic patterns (Chaudhary et al. , 2016, Costelloet al. , 2017, Miller et al. , 2018, Menegotto and Rangel,
2018, Gagné et al. , 2020), are at least minimally suitable for
studying diversity dynamics at regional scales – even in
underrepresented groups such as bivalved molluscs. Additionally,
temperature was found to be the main predictor of taxonomic diversity in
the field data, with spatial autoregression explaining 67% of the
variation, consistent with previous studies (Barneche et al. ,
2019, Saeedi et al. , 2019, Gagné et al. , 2020). Abiotic
variables could not predict diversity patterns to the same extent in the
OBIS data: the variation explained in models based on those data was
half as great. This is likely due to the smaller individual sample sizes
in cells for the OBIS dataset. Our field dataset may be a better
reflection of true latitudinal diversity patterns because our sampling
effort was uniform and intense.
Neither the OBIS data nor the field data are consistent with latitudinal
patterns for bivalves seen in other continents (Roy et al. , 1994)
or in global reconstructions (Chaudhary et al. , 2016), with a
much smoother gradient and no stepwise change that matches a provincial
boundary. On the other hand, the biogeographic gradient seen in the
field and OBIS data is broadly consistent with published regional
biogeographic schemes (Ebach et al. , 2013, Wilson and Allen,
1971), including two provinces, and is similar to Australian provincial
patterns shown in global schemes (Costello et al. , 2017). The
biogeographic interpretation in our data is that bivalves do not form
clear clusters along the Australian coastline, but a long transition
that spans a biogeographic boundary.
The biogeographic transition is robust at both species and family level
(Fig. 3, Fig. S3), with northern and southern provinces having distinct
proportional composition (Fig. 4) despite most families being present at
every field site. Ebach et al. (2013) list the upper limit of the
Peronian (a province containing NSW and Victoria) as -32.7º, which falls
at the start of the transition zone seen here in both datasets. However,
little attention has been drawn to the presence of the transitional
gradient itself, and both global and local assessments tend to agree on
a two-cluster scheme (Ebach et al. 2013, Costello et al.2017).
Historically, transitions have either been recognised as overlapping
biotic zones or as mixing zones (Hermogenes De Mendonça and Ebach,
2020). Here, a gradient between two tight clusters can be seen across
datasets, with temperature being able to explain it in most cases (Table
S3). A change in beach geomorphology along the transition zone (Shortet al. , 2000, Short et al. , 2007) may be a contributing
factor, but further research is needed to fully determine the histories
of the zones in order to assess the relationship.
Here we show that latitudinal gradients seen in data downloaded from
OBIS match those shown in field data – confirming their comparability
in diversity studies. OBIS data are less useful, however, for recreating
local patterns, where the presence and strength of the gradient are
largely dependent on the choice of diversity measure. Adding
observational data weakens or removes any clear latitudinal signal,
which is likely to be of concern when data sets are largely made up of
such information. At the same time, latitudinal and biogeographic
patterns were uncovered here using a relatively small number of field
sites. Thus, we suggest that regional diversity patterns can be
quantified easily using well-spaced, high-intensity sampling to
supplement existing databases.
Acknowledgments
This study was funded by an international Macquarie University Research
Excellence Scholarship (iMQRES; number 2016343 to MRK). The authors
would like to thank Laura Aranda Fernández, Kelly-Anne Lawler, Jim
McLean, Joshua Nito, Panayiotis Panaretos, Kathleen Perry, and Aaron
Phillips for field assistance. Bonnie Ngata and Sofia Zvolanek provided
lab assistance. Fieldwork in national parks and protected areas was
carried out in Queensland under a Marine Park Permit (MPP19-002001) and
in New South Wales under a Section 37 Collection Permit (P18/0013-1.0).