Terrestrial discharge influences microbioerosion and microbioeroder community structure in coral reefs

Microbioerosion rates and microbioeroder community structure were studied in four Kenyan protected coral-reef lagoons using shell fragments of Tridacna giant clams to determine their response to the influence of terrestrial run-off. Fourteen different microbioeroder traces from seven cyanobacteria, three green algae and four fungi species were identified. The river discharge-impacted reef and ‘pristine’ reef showed similar composition but higher microbioeroder abundance and total cyanobacteria- and chlorophyte-bioeroded areas when compared with the other study reefs. Cyanobacteria dominated during the north-east monsoon (NEM) relative to the south-east monsoon (SEM) season, with algae and cyanobacteria being major microbioeroders in the river-impacted and pristine reefs. The rate of microbioerosion varied between 4.3 g CaCO3 m^?2 y^?1 (SEM) and 134.7 g CaCO₃ m^?2 y^?1 (NEM), and was highest in the river-impacted reef (127.6 g CaCO₃ m^?2 y^?1), which was almost double that in the pristine reef (69.5 g CaCO₃ m^?2 y^?1) and the mangrove-fringed reef (56.2 g CaCO₃ m^?2 y^?1). The microbioerosion rates measured in this study may not be high enough to cause concern with regard to the health and net carbonate production of Kenya’s coral reefs. Nevertheless, predicted increases in the frequency and severity of stresses related to global climate change (e.g. increased sea surface temperature, acidification), as well as interactions with local disturbances and their influence on bioerosion, may be increasingly important in the future.     

The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets

Increasingly, the geographically weighted regression (GWR) model is being used for spatial prediction rather than for inference. Our study compares GWR as a predictor to (a) its global counterpart of multiple linear regression (MLR); (b) traditional geostatistical models such as ordinary kriging (OK) and universal kriging (UK), with MLR as a mean component; and (c) hybrids, where kriging models are specified with GWR as a mean component. For this purpose, we test the performance of each model on data simulated with differing levels of spatial heterogeneity (with respect to data relationships in the mean process) and spatial autocorrelation (in the residual process). Our results demonstrate that kriging (in a UK form) should be the preferred predictor, reflecting its optimal statistical properties. However the GWR-kriging hybrids perform with merit and, as such, a predictor of this form may provide a worthy alternative to UK for particular (non-stationary relationship) situations when UK models cannot be reliably calibrated. GWR predictors tend to perform more poorly than their more complex GWR-kriging counterparts, but both GWR-based models are useful in that they provide extra information on the spatial processes generating the data that are being predicted.     

Cape cormorants decrease,move east and adapt foraging strategies following eastward displacement of their main prey

Numbers of Cape cormorants Phalacrocorax capensis breeding in South Africa decreased by nearly 50% from approximately 107 000 pairs in 1977–1981 to 57 000 pairs in 2010–2014. Although four colonies had >10 000 pairs in 1977–1981, there was just one such colony in 2010–2014. Almost all the decrease occurred after the early 1990s off north-west South Africa, between the Orange River estuary and Dassen Island. South of this, the number breeding in the two periods was stable, with some colonies being formed or growing rapidly in the 2000s. The proportion of South Africa’s Cape cormorants that bred south of Dassen Island increased from 35% in 1977–1981 to 66% in 2010–2014, with the opposite situation observed in the north-west. This matched a shift to the south and east in the distributions of two of the Cape cormorant’s main prey species, anchovy Engraulis encrasicolus and sardine Sardinops sagax. In 2014, an apparent scarcity of prey in the north-west resulted in Cape cormorants attempting to take bait from hooks of fishing lines over an extended period, a behaviour not previously recorded. The number of Cape cormorants breeding in the south may be constrained by the absence of large islands between Dyer Island in the west and Algoa Bay in the east. If so, it may be possible to bolster the southern population through the provision of appropriate breeding habitat, such as platforms, or restricting human disturbance at suitable mainland cliff breeding sites.     

Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data

Geographically weighted regression (GWR) is an important local technique for exploring spatial heterogeneity in data relationships. In fitting with Tobler’s first law of geography, each local regression of GWR is estimated with data whose influence decays with distance, distances that are commonly defined as straight line or Euclidean. However, the complexity of our real world ensures that the scope of possible distance metrics is far larger than the traditional Euclidean choice. Thus in this article, the GWR model is investigated by applying it with alternative, non-Euclidean distance (non-ED) metrics. Here we use as a case study, a London house price data set coupled with hedonic independent variables, where GWR models are calibrated with Euclidean distance (ED), road network distance and travel time metrics. The results indicate that GWR calibrated with a non-Euclidean metric can not only improve model fit, but also provide additional and useful insights into the nature of varying relationships within the house price data set.     

Vertical distribution of living mangrove foraminifera from KwaZulu-Natal,South Africa

Modern foraminiferal assemblage zones can be used to reconstruct palaeo sea levels when applied to fossil foraminifera down a sediment core. Previous intertidal foraminiferal studies have predominantly focused on assemblages in surface sediments (0–1 cm), with the rationale that surface assemblages reflect the modern-day environment. Foraminifera live infaunally and therefore there is a need to document the infaunal vertical distribution of living foraminifera to fully capture the modern environment. Infaunal foraminiferal populations may compositionally differ from or be similar to those in the uppermost 1 cm of a core sample, but abundance is variable vertically, making it very complex to reconstruct and interpret past sea levels. This can have implications for the choice of assemblages to use as modern analogues for past sea-level reconstructions. This study documents the vertical infaunal distribution of living foraminifera, to allow for more informed interpretations of palaeo-reconstructions in mangrove environments. The down-core vertical distribution and abundance of living foraminifera, along with grain size and organic content, were documented using sediment cores along an elevational transect. Nine taxa were recorded as living at the time of collection, six of which were restricted to the top 4 cm. The majority of these were calcareous and found in the cores situated closer to the intertidal channel. Therefore, we argue that the diversity of living calcareous and agglutinated foraminifera could be restricted by grain size, with coarser grain sizes associated with lower species diversity. The findings suggest that foraminiferal species inhabiting the top 4 cm represent deeper living foraminiferal populations. Therefore, the top 4-cm interval can be used to establish a modern training set upon which reconstructions can be based. The findings from this study will provide guidance on the use of South African mangrove environments for future sea-level reconstructions.     

Moving window kriging with geographically weighted variograms

This study adds to our ability to predict the unknown by empirically assessing the performance of a novel geostatistical-nonparametric hybrid technique to provide accurate predictions of the value of an attribute together with locally-relevant measures of prediction confidence, at point locations for a single realisation spatial process. The nonstationary variogram technique employed generalises a moving window kriging (MWK) model where classic variogram (CV) estimators are replaced with information-rich, geographically weighted variogram (GWV) estimators. The GWVs are constructed using kernel smoothing. The resultant and novel MWK–GWV model is compared with a standard MWK model (MWK–CV), a standard nonlinear model (Box–Cox kriging, BCK) and a standard linear model (simple kriging, SK), using four example datasets. Exploratory local analyses suggest that each dataset may benefit from a MWK application. This expectation was broadly confirmed once the models were applied. Model performance results indicate much promise in the MWK–GWV model. Situations where a MWK model is preferred to a BCK model and where a MWK–GWV model is preferred to a MWK–CV model are discussed with respect to model performance, parameterisation and complexity; and with respect to sample scale, information and heterogeneity.     

Geographically weighted regression with parameter-specific distance metrics

Geographically weighted regression (GWR) is an important local technique to model spatially varying relationships. A single distance metric (Euclidean or non-Euclidean) is generally used to calibrate a standard GWR model. However, variations in spatial relationships within a GWR model might also vary in intensity with respect to location and direction. This assertion has led to extensions of the standard GWR model to mixed (or semiparametric) GWR and to flexible bandwidth GWR models. In this article, we present a strongly related extension in fitting a GWR model with parameter-specific distance metrics (PSDM GWR). As with mixed and flexible bandwidth GWR models, a back-fitting algorithm is used for the calibration of the PSDM GWR model. The value of this new GWR model is demonstrated using a London house price data set as a case study. The results indicate that the PSDM GWR model can clearly improve the model calibration in terms of both goodness of fit and prediction accuracy, in contrast to the model fits when only one metric is singly used. Moreover, the PSDM GWR model provides added value in understanding how a regression model’s relationships may vary at different spatial scales, according to the bandwidths and distance metrics selected. PSDM GWR deals with spatial heterogeneities in data relationships in a general way, although questions remain on its model diagnostics, distance metric specification, and computational efficiency, providing options for further research.