Paul Quataert

A zone-specific fish-based biotic index as a management tool for the Zeeschelde estuary (Belgium)

Fish-based indices monitor changes in surface waters and are a valuable aid in communication by summarising complex information about the environment (Harrison and Whitfield, 2004). A zone-specific fish-based multimetric estuarine index of biotic integrity (Z-EBI) was developed based on a 13 year time series of fish surveys from the Zeeschelde estuary (Belgium). Sites were pre-classified using indicators of anthropogenic impact. Metrics showing a monotone response with pressure classes were selected for further analysis. Thresholds for the good ecological potential (GEP) were defined from references. A modified trisection was applied for the other thresholds. The Z-EBI is defined by the average of the metric scores calculated over a one year period and translated into an ecological quality ratio (EQR). The indices integrate structural and functional qualities of the estuarine fish communities. The Z-EBI performances were successfully validated for habitat degradation in the various habitat zones.

Effects of willow stands on heavy metal concentrations and top soil properties of infrastructure spoil landfills and dredged sediment-derived sites.

The effects of willow stand development on top soil properties of uncontaminated infrastructure spoil landfills (ISL) and contaminated dredged sediment landfills (DSL) were assessed. For the ISL, significant increases in Cd, Zn and organic C levels in the top soil (0-10 cm) were detected more than 20 years after disposal. The increases in Cd and Zn concentrations in the top soil were attributed to leaf-associated metal transfer and leaf fall: the relatively high Cd and Zn concentrations in willow leaves resulted in top soil enrichment for these elements. Higher absolute amounts of Cd, Zn and Mn were taken up and recycled during leaf fall on DSL than on ISL, but did not result in significant differences between top soil and deeper soil (10-30 cm) for the DSL. Direct comparison of top soil development between both types of sites is not possible due to differences in stand age and time since disposal. The DSL were characterised by a higher short-range variance for the Cd, Cr, Cu, Pb and Zn concentrations in the top soil than the ISL. During the first years of ripening and dewatering, significant sulphate leaching occurred in the top soil of the DSL.

No emergence of Echinococcus multilocularis in foxes in Flanders and Brussels anno 2007-2008.

Echinococcus multilocularis is highly endemic in red foxes in southern Belgium (region of Wallonia), especially in the higher located forested areas. The north of Belgium, including the regions of Flanders and Brussels, is more urbanized and has been colonized entirely by red foxes since the 1980s. A temperospatial analysis of compiled epidemiological data from 1996 to 2003 predicted a northwest spread of the cestode from Wallonia and the Netherlands towards Flanders and Brussels (Prev. Vet. Med. 2006, 76, 137–150). In 2007–2008, none of 187 examined foxes from the north tested positive (<2.8%, α = 0.01), compared to 1.7% in 1996–1999. This suggests that the parasite is not emerging in the examined area and the endemic region has not significantly extended northwest during the last decade. The possible reasons are discussed in the article, including the relatively low altitude, milder climate or low abundance of suitable intermediate hosts. The low prevalence in foxes and the generally low infection rate in humans imply that the risk for public health in Flanders and Brussels is limited anno 2007–2008.

Working with population totals in the presence of missing data comparing imputation methods in terms of bias and precision

AbstractMissing observations in water bird censuses are commonly handled using the Underhill index or the birdSTATs tool which enables the use of TRIM under the hood. Multiple imputation is a standard technique for handling missing data that is rarely used in the field of ecology, but is a well known statistical technique in the fields of medical and social sciences. The purpose of this paper is to compare these three methods in terms of bias and variance. The bias in the Underhill method depends on the algorithm and starting values. birdSTATs and multiple imputation are unbiased in the case of missing values that are missing completely at random; more missing values implies less information, and so wider confidence intervals are expected as the missingness increases. The Underhill method and birdSTATs tool underestimate the variance; omitting data from a complete dataset and applying the Underhill index or birdSTATs tool results in smaller confidence intervals. Multiple imputation with an adequate imputation model provides wider confidence intervals. Biased parameter estimates with underestimated variance can potentially lead to incorrect management and policy conclusions. Hence, we dissuade the use of Underhill indices or the birdSTATs tool to handle missing data, rather we suggest that multiple imputation is a more robust alternative, even in suboptimal conditions.ZusammenfassungGesamtbestandszahlen trotz fehlender Daten – ein Vergleich von Imputationsmethoden hinsichtlich systematischer Abweichungen und Genauigkeit Fehlende Beobachtungen bei Wasservogelzählungen werden üblicherweise gehandhabt, indem der Underhill-Index oder birdSTATs angewendet werden. Letzteres nutzt TRIM. Multiple Imputation ist eine Standardmethode für die Handhabung fehlender Daten, die in der Medizin und in den Sozialwissenschaften wohlbekannt ist, in der Ökologie jedoch kaum angewendet wird. Das Ziel dieses Artikels ist es, diese drei Methoden hinsichtlich systematischer Abweichungen und Varianz zu vergleichen. Systematische Abweichungen beim Underhill-Index hängen vom Algorithmus und von den Ausgangswerten ab. birdSTATs und multiple Imputation sind frei von systematischen Fehlern, falls Daten vollkommen zufällig fehlen (MCAR). Fehlen mehr Werte, bedeutet dies weniger Information, und folglich erwarten wir umso größere Konfidenzintervalle, je mehr Werte fehlen. Der Underhill-Index und birdSTATs unterschätzen allerdings die Varianz. Lässt man aus einem an sich kompletten Datensatz Daten aus und wendet den Underhill-Index oder birdSTATs an, werden die Konfidenzintervalle kleiner. Multiple Imputation mit einem geeigneten Imputationsmodell liefert hingegen größere Konfidenzintervalle. Systematisch abweichende Parameterschätzungen mit unterschätzter Varianz führen möglicherweise zu falschem Management und Leitlinienabschlüssen. Daher raten wir vom Gebrauch des Underhill-Index oder birdSTATs zur Handhabung fehlender Daten ab. Multiple Imputation ist selbst unter suboptimalen Bedingungen eine robustere Alternative.

Reply to the comment on 'Working with population totals in the presence of missing data comparing imputation methods in terms of bias and precision' by Bogaart et al.

Bogaart et al. (2017) indicate in their comment that Onkelinx et al. (2017b) misinterpreted some aspects of the Underhill index (UIndex), Species Trends Analysis Tool for birds (birdSTATs) and TRends and Indices for Monitoring data (TRIM), and, as a consequence, do not sufficiently acknowledge the quality of those methods. We agree that some operational choices and underlying assumptions were not fully clear to us. However, if the documentation was incomplete and/or if variants existed, we made choices and filled in gaps, always in favour of Underhill and TRIM, to test our approach as thoroughly as possible and to guarantee a balanced comparison. In fact, in our paper we acknowledge that under certain circumstances, Underhill and TRIM can work properly (see the results and discussion of our paper), but our main point is that multiple imputation covers a broader range of situations and assumptions, and hence it is more flexible and robust. In practice, we cannot always be sure that the assumptions of Underhill or TRIM are valid. In these circumstances, a method that proves to be more robust is preferable. For instance, Bogaart et al. (2017) mention in their fifth point that it is still an open question within the eco-statistical community whether a negative binomial or a quasi-Poisson distribution is more appropriate. However, with multiple imputation, you can make your own choice according to the context or theoretical insight. With an analytical approach, for a new model, the source code needs to be adapted. In addition, in our paper, we caution that an appropriate model must be carefully selected, and we demonstrate what happens with a less appropriate model. In the following, we reply in greater depth to the statements in Bogaart et al. (2017). Each number corresponds to their numbering.