Changes in landscape and vegetation of coastal dunes in northwest Europe: a review

In coastal dunes, landscape changes are a rule, rather than an exception. This paper gives an overview of changes in landscape and vegetation with a focus on the past century. The history of dunes is characterised by phases of sand drift, alternated with geomorphological stability. The historical development of dune woodland during these stable phases has been documented for sites all over Europe. Vegetation reconstructions of historical open dune habitats however is very difficult due to limited preservation of fossil remains. People have drastically altered coastal dune landscapes through centuries of exploitation and more recently development of the coast. Historical land use has generally pushed vegetation back into a semi-natural state. During roughly the past century a tendency of increasing fixation and succession is observed on coastal dunes throughout northwest Europe. Six causes of change are discussed. 1) Changes in land use, mainly abandonment of agricultural practices, have led to the development of late successional stages such as scrub and woodland. 2) Crashing rabbit populations due to myxomatosis in the 1950s caused vigorous grass growth and probably stimulated scrub development. 3) A general tendency of landscape fixation is observed due to both natural and anthropogenic factors. 4) Eutrophication, mainly due to atmospheric nitrogen deposition is clearly linked to grass encroachment on acidic but also on some calcareous dunes. 5) The impact of climate change on vegetation is still unclear but probably lengthening of growing season and maybe enhanced CO₂ concentrations have led to an acceleration of succession. 6) A general anthropogenisation of the landscape occurs with rapid spread of non-native species as an important consequence. The reconstruction of a natural reference landscape is considered largely unattainable because of irreversible changes and the long tradition of human impact, in many cases since the development of the dunes. Two contradictory elements need reconciliation. First, the general acceleration of succession and scrub and woodland development in particular is partly caused by a decreased anthropogenic interference in the landscape and deserves more appreciation. Second, most biodiversity values are largely linked to open, early succession dune habitats and are threatened by the same tendency. Apart from internal nature management, in which grazing plays an important part, re-mobilisation of stable, senescent dunes is an important challenge for dune management.     

Application of a coastal model to simulate present and future inundation and aid coastal management

This paper describes the application of coastal hydro-informatic modelling (using the TELEMAC Modelling System) to address management issues arising from projected hydrodynamical and morphological changes within a shallow, sandy estuarine environment. The model incorporates the complex interaction of ocean, terrestrial and atmospheric processes. The case study of the Dyfi Estuary, on the west coast of Wales, is highlighted here. As sea levels have risen locally and are predicted to rise further, a National Nature Reserve (Borth Bog), which has been reclaimed from tidal waters by embankments, will be at increasing risk from flooding episodes due to overtopping of these embankments at high tide. Present and predicted future tidal-fluvial scenarios have been modelled in the Dyfi Estuary in order to estimate the potential for flooding. In addition, areas of greatest velocity change and potential for sediment erosion/accretion have been identified. A further process that has been investigated is how salt marsh migration is affected by sea-level rise. This case study exemplifies some fundamental and complex physical processes inherent to estuaries, and shows how different management options can be assessed, before their implementation, through a modelling approach.     

Examining the potential impacts of sea level rise on coastal wetlands in north-eastern NSW,Australia

The coastal wetlands of north-eastern New South Wales (NSW) Australia are increasingly being affected by anthropogenic factors such as urbanisation, residential development and agricultural development. However, little is known about their vulnerability to sea level rise as a result of climate change. The aim of this research is to predict the potential impact of sea level rise (SLR) on the coastal wetland communities. Sea Level Affecting Marshes Model (SLAMM) was used to predict the potential impacts of sea level rise. Geographic Information System (GIS) was used for mapping and analysis. It was found that a meter rise in sea level could decrease coastal wetlands such as Inland fresh marshes from about 225.67 km² in February 2009 to about 168.04 km² by the end of the century in north-eastern NSW, Australia. The outcomes from this research can contribute to enhancing wetland conservation and management in NSW.     

Using stochastic volatility models to analyse weekly ozone averages in Mexico City

In this paper we make use of some stochastic volatility models to analyse the behaviour of a weekly ozone average measurements series. The models considered here have been used previously in problems related to financial time series. Two models are considered and their parameters are estimated using a Bayesian approach based on Markov chain Monte Carlo (MCMC) methods. Both models are applied to the data provided by the monitoring network of the Metropolitan Area of Mexico City. The selection of the best model for that specific data set is performed using the Deviance Information Criterion and the Conditional Predictive Ordinate method.     

Modelling volatility of growth rate in atmospheric carbon dioxide concentrations in a Bayesian approach

Atmospheric gases, such as carbon dioxide, ozone, methane, nitrous oxide, and etc., create a natural greenhouse effect and cause climate change. Therefore, modelling behavior of these gases could help policy makers to control greenhouse effects. In a Bayesian framework, we analyse and model conditional variance of growth rate in atmospheric carbon dioxide concentrations (ACDC) using monthly data from a subset of the well known Mauna Loa atmosphere carbon dioxide record. The conditional variance of ACDC monthly growth rate is modelled using the autoregressive conditional heteroscedasticity (ARCH), generalized ARCH model (GARCH) and a few variants of stochastic volatility (SV) models. Smooth transition ARCH and GARCH models are shown to be able to capture the dynamics in the conditional variance in ACDC level growth rate and to improve the forecast performance of ACDC growth rate.     

Generalized extreme value regression for ordinal response data

This paper introduces a flexible skewed link function for modeling ordinal response data with covariates based on the generalized extreme value (GEV) distribution. Commonly used probit, logit and complementary log-log links are prone to link misspecification because of their fixed skewness. The GEV link is flexible in fitting the skewness in the response curve with a free shape parameter. Using Bayesian methodology, it automatically detects the skewness in the response curve along with the model fitting. The flexibility of the proposed model is illustrated by its application to an ecological survey data about the coverage of Berberis thunbergii in New England. We employ the latent variable approach by Albert and Chib (J Am Stat Assoc 88:669–679, (1993) to develop computational schemes. For model selection, we employ the Deviance Information Criterion (DIC).     

Graphical spatial models: a new view on interpreting spatial pattern

Graphical models provide an important tool for facilitating communication between scientists, decision-makers, and statisticians—many complicated ecological processes can be described in terms of “box-and-arrow” conceptual diagrams (e.g., Shipley in Cause and correlation in biology: a user’s guide to path analysis, structural equations and causal inferences, Cambridge Universtiy Press, Cambridge, 2000; Clark and Gelfand TRENDS in Ecology and Evolution 21:375–380, 2006). In particular, problems in landscape ecology often involve modeling relationships among multiple physical and/or biological variables that may operate on differing spatial scales (e.g., Rossi et al. in Ecol Monographs 62:277–314, 1992; Legendre et al. in Ecography 25:601–615, 2002; Overmars et al. in Ecol Model 164:257–270, 2003; Brown and Spector in J Appl Ecol 45:1639–1648, 2008; Koniak and Noy-Meir in Ecol Model 220:1148–1158, 2008). These problems are inherently multivariate, though researchers commonly rely on univariate methods, such as spatial regression models, to address them. In this paper, we introduce a multivariate method—graphical spatial models—that extends path analysis to incorporate spatial autocorrelation in one or more variables in a directed graph. We show how both exogenous and endogenous ecological processes as defined by Legendre et al. (Ecography 25:601–615, 2002) and Lichstein et al. (Ecol Monographs 72:445–463, 2002) can be represented in a graph. Most importantly, we show how to translate graphs representing these ecological processes into statistically estimable models. We motivate our theoretical results using an example of stream health data from the Willamette Valley, Oregon. For these data we are interested in the spatial pattern within both riparian land use and an index of stream health, and whether there is an association between land use and stream health, after accounting for these spatial patterns. We use a graphical spatial model to address these ecological questions simultaneously. We find that the health of a stream decreases as the percent of developed land within a 120-m riparian buffer increases; interestingly, there is only evidence of spatial pattern within land use.     

Lack-of-fit tests for assessing mean structures for continuous dose-response data

We review a range of lack-of-fit tests suitable for assessing the appropriateness of the mean function in dose-response models. The review encompasses both well-known tests and new tests based on recent developments in statistics, which we have extended to the dose-response case. We argue that the classical methods are inadequate in certain situations, where the new tests may be applied. Power comparisons are carried out by means of extensive simulation studies, covering both designs with and without replicates at small and large sample sizes. Three datasets from dose-response applications illustrate differences and similarities between the tests. The results suggest that the new tests perform better and exhibit a wider applicability.     

Rank regression analysis of correlated water quality data from South East Queensland

With growing population and fast urbanization in Australia, it is a challenging task to maintain our water quality. It is essential to develop an appropriate statistical methodology in analyzing water quality data in order to draw valid conclusions and hence provide useful advices in water management. This paper is to develop robust rank-based procedures for analyzing nonnormally distributed data collected over time at different sites. To take account of temporal correlations of the observations within sites, we consider the optimally combined estimating functions proposed by Wang and Zhu (Biometrika, 93:459–464, 2006) which leads to more efficient parameter estimation. Furthermore, we apply the induced smoothing method to reduce the computational burden. Smoothing leads to easy calculation of the parameter estimates and their variance-covariance matrix. Analysis of water quality data from Total Iron and Total Cyanophytes shows the differences between the traditional generalized linear mixed models and rank regression models. Our analysis also demonstrates the advantages of the rank regression models for analyzing nonnormal data.     

Enhancing submerged coastal constructions by incorporating multifunctional purposes

Surfing has becoming more and more attractive in the past few decades, constituting nowadays an important source of revenue for many countries with extensive coastlines. For this purpose and also for environmental reasons, the conventional ways of protecting a coastline appear to entail some disadvantages. An innovative and interesting way of improving surfing capacities and contributing to protect a local coastal zone is by means of multifunctional artificial reefs. A multifunctional artificial reef (MFAR) is a submerged structure that serves several purposes; in particular, it may enhance the surfing possibilities and the environmental value of the local area. This structure has some promising new aspects, too: first, it provides an unimpaired visual amenity; second, it offers tourist and economic benefits by improving the surfing conditions; third, it can enhance the environmental value of the area where it is built, and fourth, if designed properly, the down drift erosion can be minimal. An appropriate reef design in terms of ‘surfability’, i.e. the possibility to surf a wave, for the Leirosa beach, located to the south of Figueira da Foz, midway along Portugal’s West Atlantic coast, has been investigated. In order to achieve the best design several steps were conducted. First, the performance of the Boussinesq-type COULWAVE model is tested with experimental data. Next, this numerical model is used to define the best values for three design parameters: reef angle; geometry of the reef (without or with a platform), and horizontal dimensions for the appropriate geometry. A preliminary design was achieved step by step, making use of the theory and of the state of the art of multifunctional reefs. This reef geometry is used in the numerical study. In terms of ‘surfability’ and for the conditions of the local coastline of Leirosa, the following values were found for the main parameters: a reef angle of 66°; a structure height of 3.20 m; a reef geometry composed of a delta without a platform; a reef submergence of 1.50 m, and a structure seaward slope of 1:10.