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Assessing the Spatial Data Quality Paradox in the Deep-sea


Abstract and Figures

Knowledge of deep-sea environments is limited by the difficulties of using traditional sampling methods in such remote areas. Sampling the deep-sea from the ocean surface rarely yields data at a spatial scale that is helpful in understanding ecological processes or meaningful for management and conservation. One way to collect better information about the seafloor is to reduce the distance between the instruments and the seafloor. This is now possible using submersible platforms. A challenge with the use of these underwater systems is the inaccuracies associated with data positioning. Positioning high-resolution datasets accurately in an underwater geospatial context is complicated by the fact that many sources of uncertainty exist, contributing to a total propagated uncertainty (TPU) on the position. These complications are acute for acoustic remote sensing systems, in which the footprint and the resulting spatial resolution of data are a direct function of depth. While quality of deep-sea data is highly variable, it is rarely assessed or explicitly considered in marine ecological studies. In this contribution, we measured the mean TPU of bathymetric data collected during surveys performed in 2010 with a remotely operated vehicle in the Northwest Atlantic, to depths down to 3,000m. We found that TPU increases with depth, leading to a " paradox of data quality " : sensors' resolution increase with depth (i.e. when reducing the distance between the sensor and the seafloor) while sensors' positional accuracy decrease with depth. We conclude that in order to be able to accurately position high-resolution datasets in the deep-sea within the same absolute reference system, the spatial resolution of the data should be larger than the TPU. Spatial data quality of underwater datasets should always be assessed, as often only the spatial resolution side of this paradox is explicitly addressed in the literature.
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Assessing the Spatial Data Quality Paradox in the Deep-sea
Vincent Lecours1 and Rodolphe Devillers1
1 Marine Geomatics Research Lab, Department of Geography, Memorial University of Newfoundland,
Knowledge of deep-sea environments is limited by the difficulties of using traditional sampling
methods in such remote areas. Sampling the deep-sea from the ocean surface rarely yields data
at a spatial scale that is helpful in understanding ecological processes or meaningful for
management and conservation. One way to collect better information about the seafloor is to
reduce the distance between the instruments and the seafloor. This is now possible using
submersible platforms. A challenge with the use of these underwater systems is the inaccuracies
associated with data positioning. Positioning high-resolution datasets accurately in an
underwater geospatial context is complicated by the fact that many sources of uncertainty exist,
contributing to a total propagated uncertainty (TPU) on the position. These complications are
acute for acoustic remote sensing systems, in which the footprint and the resulting spatial
resolution of data are a direct function of depth. While quality of deep-sea data is highly
variable, it is rarely assessed or explicitly considered in marine ecological studies. In this
contribution, we measured the mean TPU of bathymetric data collected during surveys
performed in 2010 with a remotely operated vehicle in the Northwest Atlantic, to depths down
to 3,000m. We found that TPU increases with depth, leading to a “paradox of data quality”:
sensors’ resolution increase with depth (i.e. when reducing the distance between the sensor and
the seafloor) while sensors’ positional accuracy decrease with depth. We conclude that in order
to be able to accurately position high-resolution datasets in the deep-sea within the same
absolute reference system, the spatial resolution of the data should be larger than the TPU.
Spatial data quality of underwater datasets should always be assessed, as often only the spatial
resolution side of this paradox is explicitly addressed in the literature.
Key words: Acoustic remote sensing, data quality, total propagated uncertainty, deep-sea,
spatial resolution
Background and Relevance
The use of satellite remote sensing to study marine environments is limited by
the capacity of electromagnetic energy to penetrate water, resulting in a dearth of
knowledge of the marine environment under the first few metres of water (Solan et al.,
2003; Robinson et al., 2011). Marine habitat mapping aims to use knowledge of the
chemical, physical and biological properties of an area to understand biological
distribution in marine environments (Brown et al., 2011). One of the most commonly
used methods to map habitats is to sample these environmental properties and assess
how they influence species distribution (e.g. Freeman & Rogers, 2003). This knowledge
is then used in predictive modeling to estimate species distribution in unsampled areas
(e.g. Tong et al., 2013).
Previous research (e.g. Davies & Guinotte, 2011; Lecours et al., 2013; Rengstorf et
al., 2012, 2013) shows that the coarse resolution data usually available for deep-sea
environments do not always significantly explain the distribution of some biological
species and are not meaningful for purposes such as management. The marine habitat
mapping community needs higher resolution data to understand the real processes
driving species distribution. This is particularly true for multibeam bathymetric data
collected from acoustic systems, which have proven their value for habitat mapping
(Brown et al., 2011). Assuming the use of comparable acoustic systems, higher
resolution bathymetric data can be collected by decreasing the distance between the
sensor and the seafloor. Getting closer to the seafloor creates a smaller footprint and a
higher density of soundings on the seafloor, resulting in a finer spatial resolution of the
bathymetric data (Lurton, 2010). This can be done with the help of submersible
platforms such as remotely operated vehicles (ROV) or autonomous underwater vehicles
(AUV) (Wright, 1999). However, data collection using submersible platforms in deep-
sea environments presents important challenges, particularly in terms of positional
accuracy (Wright & Goodchild, 1997). The quality of the data directly impacts the
reliability of species-environment relationships measurements, habitat maps and
predictive models. Analyses of data quality are rarely performed in habitat mapping
studies (Barry & Elith, 2006), and it is estimated that the most important sources of
uncertainty come from data acquisition (Rocchini et al., 2011).
In this paper, we present a data quality assessment of multibeam bathymetric
data collected for deep-sea habitat mapping using a ROV. We measured the total
propagated uncertainty (TPU) of these data and their spatial resolution and compared
them with the depth of the surveys. Using these results, we discuss a data quality
paradox that occurs when collecting data in the deep-sea using submersible vehicles.
Methods and Data
High-resolution multibeam sonar, video and oceanographic data were collected
in 2010 off Newfoundland and Labrador, Canada, using the ROV ROPOS (Remotely
Operated Platform for Ocean Science) from the Canadian Scientific Submersible
Facility. Multibeam bathymetric data were collected using an Imagenex Delta-T system
mounted on the ROV. The ROV surveyed at heights varying from 1m to 50m above the
seafloor. Several instruments were used to estimate the position of the ROPOS. First, an
IXSEA GAPS ultra-short baseline (USBL) used an acoustic pulse travelling between the
supporting surface vessel and transducers mounted on the ROV to calculate the range
between them, from which a relative position was derived. This USBL had a 0.2% root
mean square (RMS) slant range accuracy. Then, a Workhorse Navigator Doppler
Velocity Log (DVL) tracked the seafloor when the height of the ROV was less than 30m.
Using speed measurements in all directions, the DVL derived a position relative to the
starting point. Long-term accuracy of the DVL was ±0.2% ±0.1cm/sec. An IXSEA
OCTANS fibre-optic gyrocompass and motion reference unit (MRU) also measured the
motion and speed of the ROV to determine its position relative to the starting point. The
accuracy of the gyrocompass was ±0.1° RMS and the accuracy of measurements from
the MRU was ±0.01° RMS. Finally, a Paroscientific Digiquartz depth sensor was used,
with an accuracy of 0.01% of the measured depth. The four sensors were used together
to improve the quality of position measurements, as often performed in the literature
(e.g. Rigby et al., 2006); the four sets of positions were merged using a Kalman Filter.
The configuration of the ROV is important to correct for multibeam bathymetric
data, as an inaccurate configuration can lead to errors in the geometric correction of the
sound beams. A survey was performed by the Canadian Hydrographic Service prior to
data collection to know the exact relative position of each sensor compared to the others
on the ROV.
All the information regarding the instruments’ errors and the ROV configuration
were entered in the bathymetric processing software CARIS HIPS and SIPS 9.0, which
was used to estimate the mean horizontal and vertical TPU on data from 14 dives. TPU
is a common measure to quantify the quality and accuracy of bathymetric data (Foster et
al., 2014). The theoretical resolution is dependent on the geometry of the multibeam
measurement; it is a function of the angular resolution of the multibeam system and the
distance to the seafloor (Lurton, 2010). The angular resolution depends on the number
of sound beams and their total angle. During these surveys, the angle was set to 120°
and 120 beams were used. We calculated the theoretical resolution using the mean
distance to the seafloor per transect analyzed, and compared it to the mean TPU as a
function of depth. There is a need to distinguish between theoretical spatial resolution,
which is the spatial resolution that can be reached based on the sensor-to-target
distance, and the practical spatial resolution, which is the greater value between the
theoretical spatial resolution and the positional uncertainty of data. To calculate the
practical resolution, we quantified the relationships between TPU and surveying depth
and theoretical spatial resolution and surveying depth. Using these equations, we
identified the intersections of the TPU curves with the theoretical spatial resolution
curves for depths ranging from 0 to 5,000m, which correspond to practical spatial
Figure 1 shows that the horizontal TPU increases linearly with the mean depth of
the surveys, ranging from a TPU of 1.8m at less than 50m deep, to more than 50m at
depths higher than 2,700m. Errors associated with measurements from the Kalman
filter contributed most to the horizontal TPU. Vertical TPU did not show any specific
relationship with depth. It ranged from 0.993m to 1.12m and was mostly influenced by
the measured depth of the ROV (79 to 97%), the heave of the platform (up to 12%), and
the alignment and timing of the MRU (up to 8%).
Figure 2 illustrates how the theoretical spatial resolution increases as the sensor-
to-target distance decreases. When the ROV was very close to the seafloor (i.e. about
1m), it enabled the collection of data at a spatial resolution of less than 2cm. The
theoretical spatial resolution could not be measured for two of the dives as the ROV
surveyed higher than 30m from the seafloor, the limit at which the DVL could track the
Using the equations generated in Figure 1 and 2, Figure 3 is an example, using a
seafloor at 3,000m deep, of how theoretical spatial resolution and horizontal TPU vary
with ROV depth. The two curves meet at 1,409m, when both the TPU and resolution are
28m. The practical spatial resolution at which to collect data at 3,000m is thus 28m,
and the ROV would need to be at 1,400m deep to keep the TPU lower than the spatial
resolution. Figure 4 extends this example to different depths and illustrates how
practical spatial resolution varies with depth.
Figure 1: Increasing propagated uncertainty (TPU) on the position of data with depth
Figure 2: Decreasing spatial resolution with distance between the sensor and the seafloor
Figure 3: Comparison between the variation of theoretical spatial resolution and TPU with
ROV depth when collecting data on a 3,000m deep seafloor
Figure 4: Variation of practical spatial resolution and appropriate surveying depth (i.e.
ROV depth) with seafloor depth
Figure 1 illustrates that the deeper the survey, the greater the positional
uncertainty, a pattern also reported by Rattray et al. (2014) in shallow waters. TPU
values in our study are lower than values from Rattray et al. study at comparable depths,
a possible result of using the Kalman filter or using a different system. Rattray et al.
(2014) showed that their USBL and MRU contribution to the TPU increased with depth,
which is also shown in our data through the Kalman filter and the MRU. Figure 2
confirms the known relationship between the spatial resolution of the resulting data and
the sensor-to-target distance (Lurton, 2010).
When trying to accurately position datasets in the same spatial reference system,
positional accuracy needs to be analyzed as the spatial resolution of data should be
larger than the uncertainty associated with data position (Moudrý & Šímová, 2012). The
red area in Figure 3 corresponds to the depths where TPU is greater than spatial
resolution, while the opposite is observed in the green area. This constraint forms the
basis of what we call a “data quality paradox” in the deep-sea: deeper submersible
surveys increase data resolution while decreasing absolute positional accuracy. The
collection of higher resolution data is therefore limited by the sampling system, its
associated TPU and the depth of the survey: Figure 4 identifies this limit as a function of
depth for the system that we used. For instance, we can find, using the calculated
equations, that if the seafloor is at 1,000m deep, we can survey with the ROV at 500m
deep and collect data at a practical resolution of 10m while being certain that the TPU
is smaller than the spatial resolution.
When quantifying relationships between variables, the influence of uncertainty
and low positional accuracy increases with the spatial resolution of the data (Hanberry,
2013). Considering that uncertainty associated with geospatial data involves a trade-off
between data quality (i.e. accuracy and precision) and spatial scale (i.e. spatial
resolution and extent), Braunisch & Suchant (2010) tried to explore which of these
characteristics should be given priority in sampling strategy. The issue is still
unresolved: some believe that a finer spatial resolution should be targeted (e.g. Reside et
al., 2012) while others think that the focus should be lowering the uncertainty (e.g.
Braunisch & Suchant, 2010). An assessment of uncertainty should be performed in
either case and this information should be documented in metadata to enable users to
consider it during analysis. It is important to remember that for some purposes there is
only a need to position datasets in a relative spatial reference system. For instance, if
datasets are collected with different sensors onboard the same submersible at the same
time and the TPU is mostly influenced by the position of the platform rather than by the
instruments themselves, it is possible to use the theoretical spatial resolution because all
data would be in the same relative reference system. On the other hand, when datasets
are collected from different instruments on different platforms that do not share the
same reference system, there is a need to position them in an absolute frame of
reference. Such absolute positioning would then be limited by the TPU and the practical
spatial resolution should be used.
Combining high-resolution geospatial datasets collected from different sensors
and platforms is commonly done when performing marine habitat mapping studies. In
order to use these datasets at their highest spatial resolutions, they need to be accurately
positioned. However, an assessment of the quality of these datasets is rarely performed
in the literature. Published works often focus on the spatial resolution of data but do not
account for positional accuracy. To our knowledge, this study is the first to compare
theoretical spatial resolution and positional uncertainty of bathymetric data and to
identify the practical spatial resolution as a function of surveying depth. A potential
solution to improve data accuracy in the deep-sea is to divide the TPU into its different
components and to try to mitigate their effects both prior to and during surveys.
Surveyors need to be aware of their system’s constraints and keep their expectations
within the limits of their data. Estimations of TPU as a function of depth should be done
prior to surveying with the instruments and configuration used, which would allow the
identification of the proper spatial resolution at which to generate the final datasets (i.e.
the practical resolution). These recommendations are only valid when the purpose of the
survey is to spatially match several datasets in an accurate, absolute geospatial context.
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... 95 % for Calvert et al., 2015). When creating the DBM, the algorithm provides vertical error estimates and statistically assigns, to each pixel, the most likely depth value based on the uncertainty of each sounding within the pixel (see Dolan and Lucieer,Figure 3. Example of elements that can be extracted and visualized when using the CUBE algorithm, using the ROV-based data set from Fig. 4 (source: Lecours and Devillers, 2015). In the top panel, the components contributing to the horizontal and vertical TPUs can be studied. ...
... Other marginal contributions to the vertical TPU included the roll and pitch of the platform, timing of the inertial measurement unit, and uncertainty associated with the sonar system (range and angle). The combination of the GPS and delta draft provides the three-dimensional position of the soundings (x, y, z); in ROV-based research, the positional accuracy decreases with depth (Lecours and Devillers, 2015). In the bottom panel, it is possible to visualize how the uncertainty and the density of soundings vary spatially. ...
... Unlike systems used in optical remote sensing, radar altimetry, and bathymetric lidar, acoustic systems do not sample the seafloor uniformly, which influences the spatial scale of the resulting DBM. The sampling density of these systems is directly dependent on depth, or more specifically on the sensor-to-seafloor distance (Lecours and Devillers, 2015). For instance, as the distance between a MBES and the seafloor increases, it takes longer for the (2014), which uses radar altimetry to fill in the gaps between higher-resolution, freely available bathymetric data. ...
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Conservation planning and management typically require accurate and spatially explicit data at scales that are relevant for conservation objectives. In marine conservation, these data are often combined with spatial analytical techniques to produce marine habitat maps. While marine habitat mapping is increasingly used to inform conservation efforts, this field is still relatively young and its methods are rapidly evolving. Because conservation efforts do not always specify standards or guidelines for the production of habitat maps, results can vary dramatically. As representations of real environmental characteristics, habitat maps are highly sensitive to how they are produced. In this review paper, I present four concepts that are known to cause variation in spatial representation and prediction of habitats: the methodology used, the quality and scale of the data, and the choice of variables in regards to fitness for use. I then discuss the potential antinomy associated with the use of habitat maps in conservation: while habitat maps have become an invaluable tool to inform and assist decision-making, maps of the same area built using different methods and data may provide dissimilar representations, thus providing different information and possibly leading to different decisions. Exploring the theories and methods that have proved effective in terrestrial conservation and the spatial sciences, and how they can be integrated in marine habitat mapping practices, could help improve maps used to support marine conservation efforts and result in more reliable products to inform conservation decisions. Having a strong, consistent, transparent, repeatable, and science-based protocol for data collection and mapping is essential for effectively supporting decision-makers in developing conservation and management plans. The development of user-friendly tools to assist in the application of such protocol is crucial to a widespread improvement in practices. I discuss the potential to use interactive and collaborative Geographic Information Systems (GIS) to encourage the conservation and management community, from data collectors and mapmakers to decision-makers, to move toward a digital resilience and to develop such science-based protocol. Until standards and protocols are developed, habitat maps should always be interpreted with care, and the methods and metadata associated with their production should always be explicitly stated.
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Remote sensing techniques are currently the main methods providing elevationdata used to produce Digital Terrain Models (DTM). Terrain attributes (e.g. slope,orientation, rugosity) derived from DTMs are commonly used as surrogates of spe-cies or habitat distribution in ecological studies. While DTMs’ errors are known topropagate to terrain attributes, their impact on ecological analyses is howeverrarely documented. This study assessed the impact of data acquisition artefacts onhabitat maps and species distribution models. DTMs of German Bank (off NovaScotia, Canada) at five different spatial scales were altered to artificially introducedifferent levels of common data acquisition artefacts. These data were used in 615unsupervised classifications to map potential habitat types based on biophysicalcharacteristics of the area, and in 615 supervised classifications (MaxEnt) to predictsea scallop distribution across the area. Differences between maps and models builtfrom altered data and reference maps and models were assessed. Roll artefactsdecreased map accuracy (up to 14% lower) and artificially increased models’ per-formances. Impacts from other types of artefacts were not consistent, eitherdecreasing or increasing accuracy and performance measures. The spatial distribu-tion of habitats and spatial predictions of sea scallop distributions were alwaysaffected by data quality (i.e. artefacts), spatial scale of the data, and the selection ofvariables used in the classifications. This research demonstrates the importance ofthese three factors in building a study design, and highlights the need for errorquantification protocols that can assist when maps and models are used in deci-sion-making, for instance in conservation and management.
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Understanding the effects of scale is essential to the understanding of natural eco - systems, particularly in marine environments where sampling is more limited and sporadic than in terrestrial environments. Despite its recognized importance, scale is rarely considered in benthic habitat mapping studies. Lack of explicit statement of scale in the literature is an impediment to better characterization of seafloor pattern and process. This review paper highlights the importance of incorporating ecological scaling and geographical theories in benthic habitat mapping. It reviews notions of ecological scale and benthic habitat mapping, in addition to the way spatial scale influences patterns and processes in benthic habitats. We address how scale is represented in geographic data, how it influences their analysis, and consequently how it influences our understanding of seafloor ecosystems. We conclude that quantification of ecological processes at multiple scales using spatial statistics is needed to gain a better characterization of species−habitat relationships. We offer recommendations on more effective practices in benthic habitat mapping, including sampling that covers multiple spatial scales and that includes as many environmental variables as possible, adopting continuum-based habitat characterization approaches, using statistical analyses that consider the spatial nature of data, and explicit statement of the scale at which the research was conducted. We recommend a set of improved standards for defining benthic habitat. With these standards benthic habitats can be defined as ‘areas of seabed that are (geo)statistically significantly different from their surroundings in terms of physical, chemical and biological characteristics, when observed at particular spatial and temporal scales’.
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This study presents an analysis of the application of underwater video data collected for training and validating benthic habitat distribution models. Specifically, we quantify the two major sources of error pertaining to collection of this type of reference data. A theoretical spatial error budget is developed for a positioning system used to co-register video frames to their corresponding locations at the seafloor. Second, we compare interpretation variability among trained operators assessing the same video frames between times over three hierarchical levels of a benthic classification scheme. Propagated error in the positioning system described was found to be highly correlated with depth of operation and varies from 1.5m near the surface to 5.7m in 100m of water. In order of decreasing classification hierarchy, mean overall observer agreement was found to be 98% (range 6%), 82% (range 12%) and 75% (range 17%) for the 2, 4, and 6 class levels of the scheme, respectively. Patterns in between-observe
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Spatial resolution and zoning affect models and predictions of species distribution models. I compared grain sizes of 90 m grid cells to ecological units of soil polygons (approximately 209 ha composed of discontinuous polygons of 16 ha), and then introduced error into samples and examined influence of topographic and soil variables. I used random forests, which is a machine learning classifier, and open access data. Predictions based on 90 m grid cells were slightly more accurate than coarser-sized polygons, particularly false positive rates (mean values of 0.11 and 0.16, respectively). The trade-off for accuracy was the number of mapping units required to increase resolution. Probability of presence decreased with resolution. Similarly to grain size comparisons, error affected probability of presence more than accuracy of prediction. Unlike grain size comparisons, the relationship between count of each species (i.e., relative abundance) and area predicted as present was lost with addition of error. Introduction of absences into the modeling sample of presences through plot location error increased probability of presence and introduction of presences into the modeling sample of absences through use of background pseudoabsences decreased probability of presence. Finer resolution amplified the effect of background absences; area predicted for presence was reduced by a factor of 5.4 for grid cells and 1.4 for soil polygons. The choice of fine resolution grid cells or coarser shaped polygons resulted in different models, due to varying influence of topographic variables on models. Use of coarser resolution (tens to hundreds of hectares) may be a worthwhile exchange for greater spatial extent of species distribution models and use of ecologically zoned polygons appeared to avoid the modifiable areal unit problem.
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Developing robust species distribution models is important as model outputs are increasingly being incorporated into conservation policy and management decisions. A largely overlooked component of model assessment and refinement is whether to include historic species occurrence data in distribution models to increase the data sample size. Data of different temporal provenance often differ in spatial accuracy and precision. We test the effect of inclusion of historic coarse-resolution occurrence data on distribution model outputs for 187 species of birds in Australian tropical savannas. Models using only recent (after 1990), fine-resolution data had significantly higher model performance scores measured with area under the receiver operating characteristic curve (AUC) than models incorporating both fine- and coarse-resolution data. The drop in AUC score is positively correlated with the total area predicted to be suitable for the species (R2=0.163–0.187, depending on the environmental predictors in the model), as coarser data generally leads to greater predicted areas. The remaining unexplained variation is likely to be due to the covariate errors resulting from resolution mismatch between species records and environmental predictors. We conclude that decisions regarding data use in species distribution models must be conscious of the variation in predictions that mixed-scale datasets might cause.
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The coral species Paragorgia arborea and Primnoa resedaeformis are abundant and widely distributed gorgonians in North Atlantic waters. Both species add significant habitat complexity to the benthic environment, and support a host of invertebrate species. Mapping their distribution is an essential step in conservation and resource manage-ment, but challenging as a result of their remoteness. In this study, three predictive models — Ecological Niche Factor Analysis, Genetic Algorithm for Rule-set Production and Maximum Entropy modeling (MaxEnt) were applied to predict the distribution of species' suitable habitat across a region of Røst Reef (Norwegian margin) based on multiscale terrain variables. All three models were successful in predicting the habitat suitability for both gorgonian species across the study area, and the MaxEnt predictions were shown to outperform other predictions. All three models predicted the most suit-able habitats for both species to mainly occur along the ridges and on the upper section of the large slide, suggesting both species preferentially colonize topographic highs. Jackknife tests for MaxEnt predictions highlighted the seabed aspect in relation to P. arborea distribution, and the seabed relative position (curvature) in relation to the distribution of both species. Given the vulnerability of deep-water corals to anthropogenic impacts, further comparative study over a wider study area would be particularly beneficial for the management of the species.
Species distribution models (SDMs) are an important tool in biogeography and ecology and are widely used for both fundamental and applied research purposes. SDMs require spatially explicit information about species occurrence and environmental covariates to produce a set of rules that identify and scale the environmental space where the species was observed and that can further be used to predict the suitability of a site for the species. More spatially accurate data are increasingly available, and the number of publications on the influence of spatial inaccuracies on the performance of modelling procedures is growing exponentially. Three main sources of uncertainty are associated with the three elements of a predictive function: the dependent variable, the explanatory variables and the algorithm or function used to relate these two variables. In this study, we review how spatial uncertainties influence model accuracy and we propose some methodological issues in the application of SDMs with regard to the modelling of fundamental and realized niches of species. We distinguish two cases suitable for different types of spatial data accuracy. For modelling the realized distribution of a species, particularly for management and conservation purposes, we suggest using only accurate species occurrence data and large sample sizes. Appropriate data filtering and examination of the spatial autocorrelation in predictors should be a routine procedure to minimize the possible influence of positional uncertainty in species occurrence data. However, if the data are sparse, models of the potential distribution of species can be created using a relatively small sample size, and this can provide a generalized indication of the main regional drivers of the distribution patterns. By this means, field surveys can be targeted to discover unknown populations and species in poorly surveyed regions in order to improve the robustness of the data for later modelling of the realized distributions. Based on this review, we conclude that (1) with data that are currently available, studies performed at a resolution of 1-100 km(2) are useful for hypothesizing about the environmental conditions that limit the distribution of a species and (2) incorporating coarse resolution species occurrence data in a model, despite an increase in sample size, lowers model performance.
The initial impetus for developing a specialty in ocean geography resulted from the need to resolve applied problems in coastal resources, as opposed to development of oceanographic research methods and concepts. However, the development in the last 10 to 20 years of sophisticated technologies for ocean data collection and management holds tremendous potential for mapping and interpreting the ocean environment in unprecedented detail. With the understanding that ocean research is often very costly, yet deemed extremely important by large funding agencies, geographers now have the opportunity to perform coastal and marine studies that are more quantitative in nature, to formulate and test basic hypotheses about the marine environment, and to collaborate with geographers working in corollary subdisciplines (e.g., remote sensing, GIS, geomorphology, political geography as pertaining to the Law of the Sea, etc.), as well as with classically-trained oceanographers. This article reviews, for the non-specialist, the newest advances in mapping and management technologies for undersea geographic research (particularly on the ocean floor) and discusses the contributions that geographers stand to make to a greater understanding of the oceans.
The distribution of vulnerable marine ecosystems in the deep sea is poorly understood. This has led to the emergence of modelling methods to predict the occurrence of suitable habitat for conservation planning in data-sparse areas. Recent global analyses for cold-water corals predict a high probability of occurrence along the slopes of continental margins, offshore banks and seamounts in the north-eastern Atlantic, but tend to overestimate the extent of the habitat and do not provide the detail needed for finer-scale assessments and protected area planning. Using Lophelia pertusa reefs as an example, this study integrates multibeam bathymetry with a wide range of environmental data to produce a regional high-resolution habitat suitability map relevant for marine spatial planning.
Recent habitat suitability models used to predict the occurrence of vulnerable marine species, particularly framework building cold-water corals, have identified terrain attributes such as slope and bathymetric position index as important predictive parameters. Due to their scale-dependent nature, a realistic representation of terrain attributes is crucial for the development of reliable habitat suitability models. In this paper, three known coral areas and a noncoral control area off the west coast of Ireland were chosen to assess quantitative and distributional differences between terrain attributes derived from bathymetry grids of varying resolution and information content. Correlation analysis identified consistent changes of terrain attributes as grain size was altered. Response characteristics and dimensions depended on terrain attribute types and the dominant morphological length-scales within the study areas. The subsequent effect on habitat suitability maps was demonstrated by preliminary models generated at different grain sizes. This study demonstrates that high resolution habitat suitability models based on terrain parameters derived from multibeam generated bathymetry are required to detect many of the topographical features found in Irish waters that are associated with coral. This has implications for marine spatial planning in the deep sea. Supplemental materials are available for this article. Go to the publisher's online edition of Marine Geodesy to view the free supplemental file.