Long-term Monitoring Datasets Research Articles

Regional avian species declines estimated from volunteer-collected long-term data using List Length Analysis

Long-term systematic population monitoring data sets are rare but are essential in identifying changes in species abundance. In contrast, community groups and natural history organizations have collected many species lists. These represent a large, untapped source of information on changes in abundance but are generally considered of little value. The major problem with using species lists to detect population changes is that the amount of effort used to obtain the list is often uncontrolled and usually unknown. It has been suggested that using the number of species on the list, the “list length,” can be a measure of effort. This paper significantly extends the utility of Franklin's approach using Bayesian logistic regression. We demonstrate the value of List Length Analysis to model changes in species prevalence (i.e., the proportion of lists on which the species occurs) using bird lists collected by a local bird club over 40 years around Brisbane, southeast Queensland, Australia. We estimate the magnitude and certainty of change for 269 bird species and calculate the probabilities that there have been declines and increases of given magnitudes. List Length Analysis confirmed suspected species declines and increases. This method is an important complement to systematically designed intensive monitoring schemes and provides a means of utilizing data that may otherwise be deemed useless. The results of List Length Analysis can be used for targeting species of conservation concern for listing purposes or for more intensive monitoring. While Bayesian methods are not essential for List Length Analysis, they can offer more flexibility in interrogating the data and are able to provide a range of parameters that are easy to interpret and can facilitate conservation listing and prioritization.

Use of dynamic soil–vegetation models to assess impacts of nitrogen deposition on plant species composition: an overview

Field observations and experimental data of effects of nitrogen (N) deposition on plant species diversity have been used to derive empirical critical N loads for various ecosystems. The great advantage of such an approach is the inclusion of field evidence, but there are also restrictions, such as the absence of explicit criteria regarding significant effects on the vegetation, and the impossibility to predict future impacts when N deposition changes. Model approaches can account for this. In this paper, we review the possibilities of static and dynamic multispecies models in combination with dynamic soil-vegetation models to (1) predict plant species composition as a function of atmospheric N deposition and (2) calculate critical N loads in relation to a prescribed protection level of the species composition. The similarities between the models are presented, but also several important differences, including the use of different indicators for N and acidity and the prediction of individual plant species vs. plant communities. A summary of the strengths and weaknesses of the various models, including their validation status, is given. Furthermore, examples are given of critical load calculations with the model chains and their comparison with empirical critical N loads. We show that linked biogeochemistry-biodiversity models for N have potential for applications to support European policy to reduce N input, but the definition of damage thresholds for terrestrial biodiversity represents a major challenge. There is also a clear need for further testing and validation of the models against long-term monitoring or long-term experimental data sets and against large-scale survey data. This requires a focused data collection in Europe, combing vegetation descriptions with variables affecting the species diversity, such as soil acidity, nutrient status and water availability. Finally, there is a need for adaptation and upscaling of the models beyond the regions for which dose-response relationships have been parameterized, to make them generally applicable.

<p class="HeadingRunIn"><strong>Distribution of <em>Magelona</em> species (Polychaeta: Magelonidae) in the German Bight (North Sea): a modeling approach</strong></p>

The aim of the present study was the development of species-habitat models for four Magelona species (Polychaeta: Magelonidae) found in the German Bight in the SE North Sea. Analyses were based on field data and data obtained from reexamination of material deposited in museum collections. In addition, data on environmental variables were retrieved from the sediment map by Figge (1981) and from long-term monitoring data sets. The statistical modeling technique applied was multivariate adaptive regression splines (MARS). Predictive accuracy measures were calculated for each model. The candidate model with highest discriminatory power was selected for predictive mapping. Models with excellent predictive performance were developed for Magelona johnstoni, M. filiformis and M. alleni based on the analyzed set of environmental predictors. In each of the developed habitat models the most important predictor was a sediment parameter, either median grain size diameter (M. johnstoni) or mud content (M. alleni, M. filiformis). Salinity and water depth were also of importance. Model predictions were aimed to allow evaluation of habitat suitability for the investigated species in the German Bight. According to our results suitable habitats for M. johnstoni are numerous and a wide distribution of this species could be expected. Habitat suitability for M. filiformis in the German Bight was suggested to be high in areas with mud contents below 10 % at water depths between 25 and 35 m. The M. alleni habitat model indicated the presence of suitable habitats where sands with elevated mud contents are present and where water depths exceed 30 m.

Development of habitat models for Nephtys species (Polychaeta: Nephtyidae) in the German Bight (North Sea)

The aim of the study was the development of habitat models for Nephtys species (Polychaeta: Nephtyidae). The investigation area was the German Bight, the southeastern part of the North Sea. Models were developed based on field data collected between 2000 and 2006. In addition, data on environmental variables were retrieved from long-term monitoring data sets and from the sediment map by Figge [Figge, K., 1981. Nordsee. Sedimentverteilung in der Deutschen Bucht. Map No. 2900. Publisher: Deutsches Hydrographisches Institut, Hamburg]. The statistical modelling technique used was multivariate adaptive regression splines (MARS). Models were fitted individually for each species. Evaluation of predictive discrimination and predictive accuracy of the developed models was by calculation of the area under the receiver operating curve (AUC) or sensitivity and specificity, respectively. Habitat models with best predictive fit were selected for the presentation of habitat suitability maps. Six Nepthys species were found: Nephtys assimilis, N. caeca, N. cirrosa, N. hombergii, N. incisa and N. longosetosa. N. hombergii was most common whereas N. incisa and N. longosetosa were rare. Habitat preferences varied considerably among the species. For all investigated Nephtys species except N. longosetosa a habitat model could be developed based on four predictor variables. The habitat models with best predictive fit were those for N. cirrosa and N. hombergii. The N. caeca habitat model was of limited predictive accuracy and only accept predictive discrimination. The number of predictors as well as the relative importance of the respective predictors in the model varied among the different species. Direct comparison of most suitable habitats for the different species based on modelling revealed that in the mostly sandy regions parallel to the German coast in water depths up to 20 m an overlap between N. caeca, N. hombergii and N. cirrosa exists. In the deeper central German Bight with mostly fine sands with increased mud contents N. hombergii, N. assimilis and, at least partially and rare in numbers, N. incisa co-occur. It can be concluded that important sediment characteristics like grain size median and mud content as well as water depth and mean salinity are useful parameters to describe the habitat requirements of most Nepthys species in the German Bight. However, additional variables need to be incorporated into such analyses.

Vulnerability of benthic habitats to the aquatic invasive species

A comparative vulnerability analysis of 16 selected benthic habitat types in the SE Baltic Sea waters and the Curonian lagoon, including Klaipeda strait, was performed using long-term monitoring datasets (1980-2003) and results of several other surveys in the lagoon and the sea. Results indicated that invasive species richness (number of alien species per habitat) in lagoon habitats was significantly higher than in the sea. Habitats formed by artificial rock and stone, sand, mud, and habi- tats modified by zebra mussel shell deposits appeared to be the most invaded. Highest invasive species richness occurred in habitats with high native species richness indicating that the main factors driving native species distribu- tion (such as favourable physical conditions, habitat alterations generated by human or/and biotic activities) are also driving aquatic invad- ers. Physical factors distinguished to be the most important for native and invasive species distribution were salinity, depth range (ex- pressed by the maximal and minimal depths difference within a habitat), shallowness of a habitat (expressed by a minimal depth), and availability of a hard substrate.

BUILDING PHENOLOGICAL MODELS FROM PRESENCE/ABSENCE DATA FOR A BUTTERFLY FAUNA

Species phenology is increasingly being used to explore the effects of climate change and other environmental stressors. Long-term monitoring data sets are essential for understanding both patterns manifest by individual species and more complex patterns evident at the community level. This study used records of 78 butterfly species observed on 626 days across 27 years at a site in northern California, USA, to build quadratic logistic regression models of the observation probability of each species for each day of the year. Daily species probabilities were summed to develop a potential aggregate species richness (PASR) model, indicating expected daily species richness. Daily positive and negative contributions to PASR were calculated, which can be used to target optimum sampling time frames. Residuals to PASR indicate a rate of decline of 0.12 species per year over the course of the study. When PASR was calculated for wet and dry years, wet years were found to delay group phenology by up to 17 days and reduce the maximum annual expected species from 32.36 to 30. Three tests to determine how well the PASR model reflected the butterfly fauna dynamics were all positive: We correlated probabilities developed with species presence/absence data to observed abundance by species, tested species' predicted phenological patterns against known biological characteristics, and compared the PASR curve to a spline-fitted curve calculated from the original species richness observations. Modeling individual species' flight windows was possible from presence/absence data, an approach that could be used on other similar records for butterfly communities with seasonal phenologies, and for common species with far fewer dates than used here. It also provided a method to assess sample frequency guidelines for other butterfly monitoring programs.

The changing ecology of temperate coastal waters during a warming trend

In the temperate North Atlantic Ocean the ecological changes in coastal waters associated with a warming period in the 1930s were compared with the past couple of decades when the North Atlantic Oscillation was also positive. Long-term monitoring data sets from Rhode Island and nearby coastal waters were used to identify trends in the recent warming period. During both events winter water temperatures warmed above a mean value of 2.9°C from 1°C to 3°C. There was no apparent trend in the annual salinity cycle correlated with the increased temperature. During both periods boreal species declined, southern species increased, and widespread declines in eelgrass occurred. Estuaries on the western Atlantic Ocean during the recent warming period had phytoplankton biomass during the winter-spring bloom decrease, zooplankton number increase, and nutrients remain elevated due to enhanced zooplankton grazing. Zooplankton numbers decreased in summer due to enhanced ctenophore predation. In these waters the loss of boreal demersal fish has been compensated by an increase in demersal decapods. The very large ecological changes caused by small increases in seasonal temperature provide an insight to the large alterations that may be associated with global warming.

Analysis of the flexural failure of an overhanging rock slab

A reservoir-induced landslide is a common geomorphological problem in the TGR (Three Gorges Reservoir) region, China. This study considers the Quchi landslide as an example of a large-scale partially reactivated ancient landslide in the TGR region; the possible kinematic responses and corresponding triggering mechanisms are investigated by analyzing eight-year monitoring data sets, field surveys, observations, and laboratory tests. The reactivated zone is primarily located in the south-western part of the ancient landslide, and it mainly moves along the deep sliding surface. The long-term monitoring data sets show that the presence of faster movements from June to September (rainy season), followed by slower deformation in the remaining nine months of the hydrological year. Both strong rainfall and drawdown during the low period of RWL (reservoir water level) are important hydrological factors responsible for the occurrence of faster movements. An RWL of 160 m a.s.l. (above sea level) during drawdown is considered the key water level for the initiation of faster movements. The motion pattern is primarily retrogressive, and the displacement velocity increases towards the toe. At present, the stability of the reactivated zone is indeterminate; a multistage failure with significant retrogressive evolution can possibly occur in the future. The findings in this study can help in managing reservoir landsides and designing landslide reinforcements in the regions of the word with geological settings similar to that of the TGR region.

DYNAMICS OF WINTER CHLOROPHYLL A IN THE KISO AND NAGARA RIVER ESTUARIES

In some lakes and estuaries, yearly maximum chlorophyll a (Chl. a) concentration was observed in winter, however, the mechanism of such blooms are poorly understood. In this study, dynamics of Chl. a of the Kiso and Nagara River estuaries was investigated, by using long-term monitoring data set of water quality during 1995-1998. The chloronity (Cl concentration) was found to be a primary component to govern the temporal variation of the Chl.a concentration in the surface layer of the two estuaries. A mathematical model was constructed to analyze the phytoplankton dynamics as a function of Cl, light intensity, nutrient (DIN), and water temperature. The model calculations satisfactorily reproduced not only year to year variations in the Kiso River estuary, but also spatial difference between the two observational sites located in the Nagara River estuary, in case that nutrient limitation was incorporated.

Development and testing of a rapid appraisal wetland condition index in south-eastern Australia

The high costs and lengthy time commitments associated with traditional monitoring, and the remoteness of many wetlands have necessitated the development of techniques for rapid wetland appraisal. A rapid appraisal condition index based on four attributes of wetlands, soil, fringing vegetation, aquatic vegetation and water quality, was developed for assessing the health of permanent floodplain wetlands in the Murray-Darling Basin of south-eastern Australia. The index, composed of 13 indicators related to wetland function, was tested in the field for scientific validity relative to an independent long-term monitoring data set, replicability of indicator scores by different investigators and the responses to the seasonality in wetland processes. Indicator values were based on a mixture of visual estimates and measurements using simple instruments or procedures and all data could be collected in the field in less than 3 h. There was a significant positive correlation between rankings of the condition of 10 wetlands based on an independent long-term monitoring data set and the wetland condition index. There were also highly significant positive correlations between indicator scores collected by different investigators. Indicator scores for physical factors and fringing vegetation did not differ between autumn and winter, but winter rainfall had a significant impact on aquatic vegetation and water quality indicators. The results indicate that the wetland condition index is a valuable and reliable tool for the rapid surveying of the condition of permanent floodplain wetlands.

Correlation between Energy Spectral States and Fast Time Variability and Further Evidence for the Marginally Stable Orbit in 4U 1820−30

We report the results from a long-term monitoring data set obtained with the Rossi X-Ray Timing Explorer (RXTE) from the bright globular cluster binary source 4U 1820-30. During this monitoring, the source flux varied from 2.9?10 -->?9 to 8.5?10 -->?9 ergs cm-2 s-1 in the 2-10 keV band, which correspond to, respectively, count rates of 1600 and 4500 counts s-1 in the nominal 2-60 keV RXTE proportional counter array band. Its energy spectral state was observed to encompass both the banana branches and the island state. Kilohertz quasi-periodic oscillations (kHz QPOs) were observed to exist in the upper banana and lower banana branches, as well as in the island state. In particular, we report that the kHz QPOs were present only when the soft color (defined as the flux ratio of the 3.9-5.0 keV band to the 1.7-3.9 keV band) was below 0.78 without regard to the overall count rate and without regard to whether the source is in any one of the three branches or states. Their centroid frequency was well correlated with the overall count rate below 2500 counts s-1. When the count rate was above 2500 counts s-1, the QPO frequency was consistent with a constant independent of the overall count rate. We take this as further evidence that the inner edge of the accretion had reached the marginally stable orbit, and therefore the constant 1060?20 Hz is the marginally stable orbit frequency.

Modeling the Concentration of Aluminum in Surface Waters

Although the increased mobilization of aluminum from soils to surface waters is widely recognized as one of the most important ecological effects of acidic deposition, lumped-parameter mathematical models of acidification response typically overestimate the change in Al concentration under changing deposition by a considerable margin. The assumption of equilibrium with gibbsite (Al(OH)3) in the MAGIC model and other models of acid-base chemistry is shown to be inconsistent with measured values for a large variety of lake and stream databases. A modified algorithm for predicting Al concentration, based on empirical relationships evident in field data, provided superior estimates of changes in Al concentration in three long-term monitoring data sets and under experimental conditions at two experimental watershed manipulation sites.