Marine habitat mapping in support of Marine Protected Area management in a subarctic fjord: Gilbert Bay, Labrador, Canada

This paper presents an approach that allows production of benthic substrate and habitat maps in fjord environments. This approach is used to support the management of the Gilbert Bay Marine Protected Area (MPA) in southeastern Labrador, Atlantic Canada. Multibeam sonar-derived bathymetry, seabed slope, and acoustic reflectance (backscatter) were combined using supervised classification methods and GIS with ground-truthed benthic sampling in order to derive maps of the substrates and main benthic habitats. Six acoustically distinct substrate types were identified in the fjord, and three additional substrate types without a unique acoustic signature were recognized. Ordination by multidimensional scaling and analysis of similarity generalized these to four acoustically distinct habitat types. Greatest within-habitat (alpha) diversity was found in the coralline-algae encrusted gravel habitat. Greatest between-habitat (beta) diversity was found in the management Zones 1 and 2, which have the highest level of protection. The study confirmed that the zoning plan for the MPA, which was designed to protect spawning and juvenile fish habitat for a local genetically distinct population of Atlantic cod, afforded highest levels of protection to areas with highest habitat diversity.     

Using earth observation data from multiple sources to map rare habitats in a coastal conservation area

Kenfig NNR (National Nature Reserve) is a coastal sand dune system in south Wales, UK. The site is an important location for the conservation of the fen orchidLiparis loeselii, a significant proportion of the UK population is found solely on the site. Approaches to the mapping and monitoring of the habitats at Kenfig NNR using EO (Earth Observation) methods are investigated. Typical airborne EO missions over such sites produce more than a single source of EO data; these may include various optical imaging sensors with different spectral ranges, film cameras and ranging devices to measure topography. Conservation managers are thus presented with the problem of which sources of data to use when producing a land cover map of the site of interest. Using a data set gathered over the Kenfig NNR site, we investigate land cover mapping methods for conservation. The land cover types of interest typically cover small areas within a much larger site so they present a hard problem for the EO data and associated classification methods to solve. Land cover classifications produced from the data sets provide a set of competing hypotheses of land cover type for the site. Methods we use to resolve this competition between the data sets include voting methods, data fusion methods and a method utilising fuzzy logic to aggregate information. This paper is intended to act as an introduction to some of the issues involved in using EO data for habitat mapping in highly heterogeneous coastal dune environments and to present some preliminary results of the performance of each method.     

Biological information from an Acoustic Doppler Current Profiler

Acoustic Doppler Current Profilers (ADCPs) are now used routinely by physical oceanographers. A by-product of the signal processing for calculating the Doppler shift used to determine current velocity is the intensity of the acoustic backscatter, and this, in turn, is a measure of biomass. ADCPs can therefore potentially provide biological oceanographers with quantitative information, collected routinely and non-intrusively in near real time. If these acoustic measurements are obtained concurrently with hydrographic data, the possibility of collecting quantitative biological information on the same time and space scales as physicochemical data becomes a reality. ADCPs have been routinely operated on NERC ships for several years. Examples of the types of biological information which can be obtained are described here in the hope that this will stimulate other users to make use of a potentially valuable source of data.     

Counting of visitors in the Meijendel dunes, The Netherlands

Accurate and sufficiently detailed information about recreation-related use of dune areas is necessary for their management. Long-term monitoring can provide this information. This paper presents the visitor counting programme used in the Meijendel dune area since 1992. The data collected during the first ten years are used to evaluate the method. The combination of mechanical vehicle counts and additional visual counts proved to be reliable and produced an accurate data set. Costs could be reduced through eventually reducing the number of counting locations and limiting the number of visual counts.     

Automatic construction of concept hierarchies: The case of foliage-dwelling spiders

In this paper, we present the hierarchical variable dependencies that were obtained from raw data with the use of two machine learning techniques on an ecological data set. The data set contains features of field margins and the corresponding number of spider species inhabiting them. This data set was used before by domain experts to construct a fuzzy qualitative model with hierarchical variable dependencies, which we use for comparison with our results. One of the machine learning methods constructs a hierarchical structure similar to the one in the experts’ model, while revealing some additional interesting relations of environmental features with respect to the number of spider species. The other method constructs a different hierarchy from the one proposed by the experts, which, according to our classification performance experiments, might be even more appropriate.     

Accounting for multiple testing in the analysis of spatio-temporal environmental data

The statistical analysis of environmental data from remote sensing and Earth system simulations often entails the analysis of gridded spatio-temporal data, with a hypothesis test being performed for each grid cell. When the whole image or a set of grid cells are analyzed for a global effect, the problem of multiple testing arises. When no global effect is present, we expect $$ \alpha $$% of all grid cells to be false positives, and spatially autocorrelated data can give rise to clustered spurious rejections that can be misleading in an analysis of spatial patterns. In this work, we review standard solutions for the multiple testing problem and apply them to spatio-temporal environmental data. These solutions are independent of the test statistic, and any test statistic can be used (e.g., tests for trends or change points in time series). Additionally, we introduce permutation methods and show that they have more statistical power. Real-world data are used to provide examples of the analysis, and the performance of each method is assessed in a simulation study. Unlike other simulation studies, our study compares the statistical power of the presented methods in a comprehensive simulation study. In conclusion, we present several statistically rigorous methods for analyzing spatio-temporal environmental data and controlling the false positives. These methods allow the use of any test statistic in a wide range of applications in environmental sciences and remote sensing.     

Diving deep in a foraging hotspot: acoustic insights into bottlenose dolphin dive depths and feeding behaviour

To exploit resources in their environment, odontocete cetaceans have evolved sophisticated diving abilities to allow effective foraging. However, data on the diving behaviour and underwater foraging behaviour remains limited. This study made use of echolocation clicks and other calls to study the diving behaviour of bottlenose dolphins. Dolphins used the full water column and consistently dived to depths of around 50 m, close to the seabed. However, the majority of their time appeared to be spent within the surface layers of the water column. In addition, by localising calls that have been associated with prey capture events (Janik, Proc R Soc Lond Ser B 267:923–927, 2000a), it appeared that certain forms of feeding behaviour occurred primarily at depths of between 20 and 30 m. Furthermore, data on the depth of clicks made before and after these feeding calls suggested that during the minute before the calls, dolphins were consistently diving from the surface to depths close to the seabed, and were subsequently returning to the surface after the calls. This passive acoustic technique proved an accurate method for studying the depth distribution of dolphin vocalisations. By exploiting the natural sounds made by these wild odontocetes, this investigation provided a previously unavailable perspective on the the 3D nature of bottlenose dolphins foraging behaviour. It confirmed that while the dolphins spent the majority of time close to the surface, the full water column was exploited during foraging events.     

Radial basis function networks with partially classified data

The problem of estimating a classification rule with partially classified observations, which often occurs in biological and ecological modelling, and which is of major interest in pattern recognition, is discussed. Radial basis function networks for classification problems are presented and compared with the discriminant analysis with partially classified data, in situations where some observations in the training set are unclassified. An application on a set of morphometric data obtained from the skulls of 288 specimens of Microtus subterraneus and Microtus multiplex is performed. This example illustrates how the use of both classified and unclassified observations in the estimate of the hidden layer parameters has the potential to greatly improve the network performances.     

Optimal model averaging by data perturbation for spatial noisy data

Geostatistical models play an important role in spatial data analysis, in which model selection is inevitable. Model selection methods, such as AIC and BIC, are popular for selecting appropriate models. In recent years, some model averaging methods, such as smoothed AIC and smoothed BIC, are also applied to spatial data models. However, the corresponding averaging estimators are outperformed by optimal model averaging estimators (Hansen in Econometrica 75:1175–1189, 2007) for the ordinary linear models. Therefore, this paper focuses on the optimal model averaging method for geostatistical models. We propose a weight choice criterion for the model averaging estimator on the basis of the generalized degrees of freedom and data perturbation technique. We further theoretically prove the resultant estimator is asymptotically optimal in terms of the mean squared error, and numerically demonstrate its satisfactory performance. Finally, the proposed method is applied to a mercury data set.     

Spatio-temporal variability in the distribution of epi- and meso-pelagic acoustic backscatter in the Irminger Sea,North Atlantic,with implications for predation on<Emphasis Type="Italic"> Calanus finmarchicus</Emphasis>

The deep scattering layers of the North Atlantic, including the Irminger Sea, contain many predators of the key copepod species Calanus finmarchicus. Previous seasonally restricted studies have described the deep acoustic scattering layers of the Irminger Sea as ubiquitous. They have shown that the intensity of the acoustic backscatter varies across the region, and so, by implication, the potential predation pressure on C. finmarchicus also varies spatially. This paper reports observations of the distribution of epi-pelagic (0–200 m) and meso-pelagic (200–1,000 m) acoustic backscatter in the Irminger Sea, made using a scientific echosounder operating at 38 kHz during four seasonal (winter, spring and summer) cruises. Our study demonstrates that the intensity of the backscatter varies seasonally in regionally distinct ways across the Irminger Sea. The mean acoustic backscatter, both in the upper 800 m and upper 200 m of the water column, varied significantly between the northern, central and southern areas of the central basin (open ocean), and within each area between the Greenland shelf slope, open ocean and Mid-Atlantic Ridge subregions. Different patterns of seasonal change in the acoustic backscatter between the upper 800 m and upper 200 m were also seen, indicating both persistent differences in the underlying amount of backscatter in each area, and varying patterns of seasonal increase in the near surface backscatter in the different areas and subregions. These observations could be related to the different oceanographic regimes encountered in each location, and are discussed in terms of their implications for potential predation pressure on C. finmarchicus.Communicated by J.P. Thorpe, Port Erin     

Multimodal signaling in the Small Torrent Frog (Micrixalus saxicola) in a complex acoustic environment

Many animals use multimodal (both visual and acoustic) components in courtship signals. The acoustic communication of anuran amphibians can be masked by the presence of environmental background noise, and multimodal displays may enhance receiver detection in complex acoustic environments. In the present study, we measured sound pressure levels of concurrently calling males of the Small Torrent Frog (Micrixalus saxicola) and used acoustic playbacks and an inflatable balloon mimicking a vocal sac to investigate male responses to controlled unimodal (acoustic) and multimodal (acoustic and visual) dynamic stimuli in the frogs’ natural habitat. Our results suggest that abiotic noise of the stream does not constrain signal detection, but males are faced with acoustic interference and masking from conspecific chorus noise. Multimodal stimuli elicited greater response from males and triggered significantly more visual signal responses than unimodal stimuli. We suggest that the vocal sac acts as a visual cue and improves detection and discrimination of acoustic signals by making them more salient to receivers amidst complex biotic background noise.     

Sine-skewed axial distributions with an application for fallen tree data

An axis is an undirected line where there is no reason to distinguish one end of the line from the other. Phenomena in nature that can be described as axial data are numerous. In this paper a method of trigonometric moments for the axial normal or axial von Mises distribution as an alternative to the method by Arnold and SenGupta (Environ Ecol Stat 13:271–285, 2006) is discussed. Sine-skewed axial Jones–Pewsey, von Mises and wrapped Cauchy distributions are introduced as special cases of a more general construction of skew axial distributions. As an example the methods are applied to a data set which consists of the orientations of logs on the floor of a primeval spruce forest.     

A building-based data capture and data mining technique for air quality assessment

Recently, a building-based air quality model system which can predict air quality in front of individual buildings along both sides of a road has been developed. Using the Macau Peninsula as a case study, this paper shows the advantages of building-based model system in data capture and data mining. Compared with the traditional grid-based model systems with input/output spatial resolutions of 1–2 km, the building-based approach can extract the street configuration and traffic data building by building and therefore, can capture the complex spatial variation of traffic emission, urban geometry, and air pollution. The non-homogeneous distribution of air pollution in the Macau Peninsula was modeled in a high-spatial resolution of 319 receptors·km^-2. The spatial relationship among air quality, traffic flow, and urban geometry in the historic urban area is investigated. The study shows that the building-based approach may open an innovative methodology in data mining of urban spatial data for environmental assessment. The results are particularly useful to urban planners when they need to consider the influences of urban form on street environment.     

Using data augmentation via the Gibbs Sampler to incorporate missing covariate structure in linear models for ecological assessments

Missing covariate values in linear regression models can be an important problem facing environmental researchers. Existing missing value treatment methods such as Multiple Imputation (MI), the EM algorithm and Data Augmentation (DA) have the assumption that both observed and unobserved data come from the same distribution, most commonly a multivariate normal or a conditionally multivariate normal family. These methods do try to incorporate the missing data mechanism and rely on the assumption of Missing At Random (MAR). We present a DA method which does not rely on the MAR assumption and can model missing data mechanisms and covariate structure. This method utilizes the Gibbs Sampler as a tool for incorporating these structures and mechanisms. We apply this method to an ecological data set that relates fish condition to environmental variables. Notice that the presented DA method detects relationships that are not detected when other missing data methods are employed.

Functional autoregressive forecasting of long-term seabed evolution

There is a need for decadal predictions of the seabed evolution, for example to inform resurvey strategies when maintaining navigation channels. The understanding of the physical processes involved in morphological evolution, and the viability of process models to accurately model evolution over these time scales, are currently limited. As a result, statistical approaches are used to supply long-term forecasts. In this paper, we introduce a novel statistical approach for this problem: the autoregressive Hilbertian model (ARH). This model naturally assesses the time evolution of spatially-distributed measurements. We apply the technique to a coastal area in the East Anglian coast over the period 1846 to 2002, and compare with two other statistical methods used recently for seabed prediction: the autoregressive model and the EOF model. We evaluate the performance of the three methods by comparing observations and predictions for 2002. The ARH model enables a reduction of 10% of the root mean squared errors. Finally, we compute the variability in the predictions related to time sampling using the jackknife, a method that uses subsamples to quantify uncertainties.     

Estimation of population density by spatially explicit capture-recapture analysis of data from area searches

The recent development of capture-recapture methods for estimating animal population density has focused on passive detection using devices such as traps or automatic cameras. Some species lend themselves more to active searching: a polygonal plot may be searched repeatedly and the locations of detected individuals recorded, or a plot may be searched just once and multiple cues (feces or other sign) identified as belonging to particular individuals. This report presents new likelihood-based spatially explicit capture-recapture (SECR) methods for such data. The methods are shown to be at least as robust in simulations as an equivalent Bayesian analysis, and to have negligible bias and near-nominal confidence interval coverage with parameter values from a lizard data set. It is recommended on the basis of simulation that plots for SECR should be at least as large as the home range of the target species. The R package "secr" may be used to fit the models. The likelihood-based implementation extends the spatially explicit analyses available for search data to include binary data (animal detected or not detected on each occasion) or count data (multiple detections per occasion) from multiple irregular polygons, with or without dependence among polygons. It is also shown how the method may be adapted for detections along a linear transect.     

Development of rapid,cost effective coral survey techniques: tools for management and conservation planning

Coral reefs are highly dynamic and productive marine ecosystems, providing habitat and refuge for an enormous number of species including fish, invertebrates and algae. With increased anthropogenic pressures and global climate change, many coral reefs are rapidly declining. Currently, there is limited knowledge on condition and community assemblage composition of shallow fringing coral reefs along the south-eastern coast of Queensland, Australia. With increased demand to determine existence of coastal fringing reefs by National Regional Management groups, a rapid cost effective method to determine reef composition and condition was required. The aim of this study was to determine the benthic structure and extent of two small coastal fringing reefs (Hummock Hill Reef and Stringers Reef) along the Southern Great Barrier Reef. Reef substrate assessments were carried out using a rapid assessment technique and a Point Intercept Method (PIM). The data were analysed and classified using a Geographic Information System (GIS). Percent substrate cover was calculated using a visual basic image analysis program. The Point intercept method showed higher accuracy over the rapid assessment technique (up to 15–40% difference) and was thus deemed a more suitable classification tool for reefs with high structural complexity and heterogeneity. This study focused on piloting a rapid, cost effective Point Intercept Technique using random point count methodology to document coral benthic habitat and extent over a commonly used rapid assessment method as a tool for reef coastal management and conservation. The two techniques were compared and substrate classification success, limitations and errors were discussed.     

Network-based exploration and visualisation of ecological data

Networks – structured graphs consisting of sets of nodes connected by edges – provide a rich framework for data visualisation and exploratory analyses. Although rarely used for the visualisation of ecological data, networks are well suited to this purpose, including data that one might not normally think of as a network. We present a simple method for transforming a data matrix into network format, and show how this can be used as the basis for interactive exploratory analyses of ecological data.The method is demonstrated using a database of marine zooplankton samples acquired in the Southern Ocean. The network analyses revealed zooplankton community structures that are in good agreement with previously published results. Variations in community structure were observed to be related to the temporal and spatial pattern of sampling, as well as to physical environmental factors such as sea ice cover. The analyses also revealed a number of errors in the data, including taxon identification errors and instrument failures.The method allows the analyst to generate networks from different combinations of variables in the data set, and to examine the effects of varying parameters such as the scales of spatial, temporal, and taxonomic aggregation. This flexibility allows the analyst to rapidly gain a number of perspectives on the data and provides a powerful mechanism for exploration.     

Using historical spy satellite photographs and recent remote sensing data to identify high-conservation-value forests

High-conservation-value forests (HCVFs) are critically important for biodiversity and ecosystem service provisioning, but they face many threats. Where systematic HCVF inventories are missing, such as in parts of Eastern Europe, these forests remain largely unacknowledged and therefore often unprotected. We devised a novel, transferable approach for detecting HCVFs based on integrating historical spy satellite images, contemporary remote sensing data (Landsat), and information on current potential anthropogenic pressures (e.g., road infrastructure, population density, demand for fire wood, terrain). We applied the method to the Romanian Carpathians, for which we mapped forest continuity (1955–2019), canopy structural complexity, and anthropogenic pressures. We identified 738,000 ha of HCVF. More than half of this area was identified as susceptible to current anthropogenic pressures and lacked formal protection. By providing a framework for broad-scale HCVF monitoring, our approach facilitates integration of HCVF into forest conservation and management. This is urgently needed to achieve the goals of the European Union's Biodiversity Strategy to maintain valuable forest ecosystems.