Aboveground plant biomass from the Jena Experiment (Monocultures, year 2008)

This data set contains aboveground plant biomass in 2008 (Sown plant community, Weed plant community, Dead plant material, and Unidentified plant material; all measured in biomass as dry weight) of the monoculture plots of a large grassland biodiversity experiment (the Jena Experiment). In the monoculture plots the biomass of the sown plant community contains only a single species per plot and this species is a different one for each plot. Which species has been sown in which plot is stated in the plot information table for monocultures (see further details below). The monoculture plots of 3.5 x 3.5 m were established for all of the 60 plant species of the Jena Experiment species pool with two replicates per species. One of the replicate plots per species was given up after the vegetation period of 2007 for all but the nine species belonging also to the so called dominance experiment in Jena. These nine species are: Alopecurus pratensis, Anthriscus sylvestris, Arrhenatherum elatius, Dactylis glomerata, Geranium pratense, Poa trivialis, Phleum pratense, Trifolium repens and Trifolium pratense.In 2008 plot size was reduced to 2.5 x 2.5 m. These 60 species comprising the species pool of the Jena Experiment belong to four functional groups (grasses, legumes, tall and small herbs). Plots were sown in May 2002 and are since maintained by bi-annual weeding and mowing. Aboveground plant biomass was harvested twice in 2008 just prior to mowing (during peak standing biomass in early June and in late August) on all experimental plots of the monocultures. This was done by clipping the vegetation at 3 cm above ground in 2 rectangles of 0.2 x 0.5 m per plot. The location of these rectangles was assigned prior to each harvest by random selection of coordinates within the core area of the plots (i.e. excluding an outer edge of 0.5 m). The positions of the rectangles within plots were identical for all plots. The harvested biomass was sorted into categories: sown plant species, weed plant species (species not sown at the particular plot), detached dead plant material (i.e., dead plant material in the data file), and remaining plant material that could not be assigned to any category (i.e., unidentified plant material in the data file). All biomass was dried to constant weight (70°C, >= 48 h) and weighed. The data for individual subsamples (i.e. rectangles) and the mean over samples for all biomass measures are given.

Aboveground plant biomass from the Jena Experiment (Monocultures, year 2009)

This data set contains aboveground plant biomass in 2009 (Sown plant community, Weed plant community, Dead plant material, and Unidentified plant material; all measured in biomass as dry weight) of the monoculture plots of a large grassland biodiversity experiment (the Jena Experiment). In the monoculture plots the biomass of the sown plant community contains only a single species per plot and this species is a different one for each plot. Which species has been sown in which plot is stated in the plot information table for monocultures (see further details below). The monoculture plots of 3.5 x 3.5 m were established for all of the 60 plant species of the Jena Experiment species pool with two replicates per species. One of the replicate plots per species was given up after the vegetation period of 2007 for all but the nine species belonging also to the so called dominance experiment in Jena. These nine species are: Alopecurus pratensis, Anthriscus sylvestris, Arrhenatherum elatius, Dactylis glomerata, Geranium pratense, Poa trivialis, Phleum pratense, Trifolium repens and Trifolium pratense.In 2008 plot size was reduced to 2.5 x 2.5 m. These 60 species comprising the species pool of the Jena Experiment belong to four functional groups (grasses, legumes, tall and small herbs). Plots were sown in May 2002 and are since maintained by bi-annual weeding and mowing. Aboveground plant biomass was harvested twice in 2009 just prior to mowing (during peak standing biomass in early June and in late August) on all experimental plots of the monocultures. This was done by clipping the vegetation at 3 cm above ground in 2 rectangles of 0.2 x 0.5 m per plot. The location of these rectangles was in the center of the plot area. The positions of the rectangles within plots were identical for all plots. The harvested biomass was sorted into categories: sown plant species, weed plant species (species not sown at the particular plot), detached dead plant material (i.e., dead plant material in the data file), and remaining plant material that could not be assigned to any category (i.e., unidentified plant material in the data file). All biomass was dried to constant weight (70°C, >= 48 h) and weighed. The data for individual subsamples (i.e. rectangles) and the mean over samples for all biomass measures are given.

Aboveground plant biomass from the Jena Experiment (Monocultures, year 2002)

This data set contains aboveground plant biomass in 2002 (Sown plant community; measured in biomass as dry weight) of the monoculture plots of a large grassland biodiversity experiment (the Jena Experiment). In the monoculture plots the biomass of the sown plant community contains only a single species per plot and this species is a different one for each plot. Which species has been sown in which plot is stated in the plot information table for monocultures (see further details below). The monoculture plots of 3.5 x 3.5 m were established for all of the 60 plant species of the Jena Experiment species pool with two replicates per species. These 60 species comprising the species pool of the Jena Experiment belong to four functional groups (grasses, legumes, tall and small herbs). Plots were sown in May 2002 and are since maintained by bi-annual weeding and mowing. Aboveground plant biomass was harvested twice in 2002 just prior to mowing (during peak standing biomass in early June and in late August) on all experimental plots of the monocultures. This was done by clipping the vegetation at 3 cm above ground in 2 rectangles of 0.2 x 0.5 m per plot. The location of these rectangles was assigned prior to each harvest by random selection of coordinates within the core area of the plots (i.e. excluding an outer edge of 0.5 m). The positions of the rectangles within plots were identical for all plots. From the harvested biomass only the separated biomass of the sown plant species was kept. All biomass was dried to constant weight (70°C, >= 48 h) and weighed. The data for individual subsamples (i.e. rectangles) and the mean over samples for all biomass measures are given.

Aboveground plant biomass from the Jena Experiment (Monocultures, year 2004)

This data set contains aboveground plant biomass in 2004 (Sown plant community, Weed plant community, Dead plant material, and Unidentified plant material; all measured in biomass as dry weight) of the monoculture plots of a large grassland biodiversity experiment (the Jena Experiment). In the monoculture plots the biomass of the sown plant community contains only a single species per plot and this species is a different one for each plot. Which species has been sown in which plot is stated in the plot information table for monocultures (see further details below). The monoculture plots of 3.5 x 3.5 m were established for all of the 60 plant species of the Jena Experiment species pool with two replicates per species. These 60 species comprising the species pool of the Jena Experiment belong to four functional groups (grasses, legumes, tall and small herbs). Plots were sown in May 2002 and are since maintained by bi-annual weeding and mowing. Aboveground plant biomass was harvested twice in 2004 just prior to mowing (during peak standing biomass in early June and in late August) on all experimental plots of the monocultures. This was done by clipping the vegetation at 3 cm above ground in 2 rectangles of 0.2 x 0.5 m per plot. The location of these rectangles was assigned prior to each harvest by random selection of coordinates within the core area of the plots (i.e. excluding an outer edge of 0.5 m). The positions of the rectangles within plots were identical for all plots. The harvested biomass was sorted into categories: sown plant species, weed plant species (species not sown at the particular plot), detached dead plant material (i.e., dead plant material in the data file), and remaining plant material that could not be assigned to any category (i.e., unidentified plant material in the data file). All biomass was dried to constant weight (70°C, >= 48 h) and weighed. The data for individual subsamples (i.e. rectangles) and the mean over samples for all biomass measures are given.

Climate change-related regime shifts have altered spatial synchrony of plankton dynamics in the North Sea

During the 1980s, the North Sea plankton community underwent a well-documented ecosystem regime shift, including both spatial changes (northward species range shifts) and temporal changes (increases in the total abundances of warmer water species). This regime shift has been attributed to climate change. Plankton provide a link between climate and higher trophic-level organisms, which can forage on large spatial and temporal scales. It is therefore important to understand not only whether climate change affects purely spatial or temporal aspects of plankton dynamics, but also whether it affects spatiotemporal aspects such as metapopulation synchrony. If plankton synchrony is altered, higher trophic-level feeding patterns may be modified. A second motivation for investigating changes in synchrony is that the possibility of such alterations has been examined for few organisms, in spite of the fact that synchrony is ubiquitous and of major importance in ecology. This study uses correlation coefficients and spectral analysis to investigate whether synchrony changed between the periods 1959–1980 and 1989–2010. Twenty-three plankton taxa, sea surface temperature (SST), and wind speed were examined. Results revealed that synchrony in SST and plankton was altered. Changes were idiosyncratic, and were not explained by changes in abundance. Changes in the synchrony of Calanus helgolandicus and Para-pseudocalanus spp appeared to be driven by changes in SST synchrony. This study is one of few to document alterations of synchrony and climate-change impacts on synchrony. We discuss why climate-change impacts on synchrony may well be more common and consequential than previously recognized.

Climate change-related regime shifts have altered spatial synchrony of plankton dynamics in the North Sea

During the 1980s, the North Sea plankton community underwent a well-documented ecosystem regime shift, including both spatial changes (northward species range shifts) and temporal changes (increases in the total abundances of warmer water species). This regime shift has been attributed to climate change. Plankton provide a link between climate and higher trophic-level organisms, which can forage on large spatial and temporal scales. It is therefore important to understand not only whether climate change affects purely spatial or temporal aspects of plankton dynamics, but also whether it affects spatiotemporal aspects such as metapopulation synchrony. If plankton synchrony is altered, higher trophic-level feeding patterns may be modified. A second motivation for investigating changes in synchrony is that the possibility of such alterations has been examined for few organisms, in spite of the fact that synchrony is ubiquitous and of major importance in ecology. This study uses correlation coefficients and spectral analysis to investigate whether synchrony changed between the periods 1959–1980 and 1989–2010. Twenty-three plankton taxa, sea surface temperature (SST), and wind speed were examined. Results revealed that synchrony in SST and plankton was altered. Changes were idiosyncratic, and were not explained by changes in abundance. Changes in the synchrony of Calanus helgolandicus and Para-pseudocalanus spp appeared to be driven by changes in SST synchrony. This study is one of few to document alterations of synchrony and climate-change impacts on synchrony. We discuss why climate-change impacts on synchrony may well be more common and consequential than previously recognized.

Scale and ecosystem services: how do observation, management, and analysis shift with scale—lessons from Québec

Ecosystem service assessment and management are shaped by the scale at which they are conducted; however, there has been little systematic investigation of the scales associated with ecosystem service processes, such as production, benefit distribution, and management. We examined how social-ecological spatial scale impacts ecosystem service assessment by comparing how ecosystem service distribution, trade-offs, and bundles shift across spatial scales. We used a case study in Québec, Canada, to analyze the scales of production, consumption, and management of 12 ecosystem services and to analyze how interactions among 7 of these ecosystem services change across 3 scales of observation (1, 9, and 75 km²). We found that ecosystem service patterns and interactions were relatively robust across scales of observation; however, we identified 4 different types of scale mismatches among ecosystem service production, consumption, and management. Based on this analysis, we have proposed 4 aspects of scale that ecosystem service assessments should consider.

Catch and effort from a recreational trolling fishery in a large lake

Over recent decades recreational fisheries have grown substantially throughout the world. Despite this increase, catches from recreational fisheries have often been ignored in fisheries management, although this is now being remedied. Monitoring recreational fisheries can be expensive, and the primary means used for monitoring is angler (creel) surveys, typically funded from sales of fishing licences. The studies presented in this thesis examine different approaches to monitoring recreational trolling fisheries’ catch and effort, where fishing licenses are not required and there are no reporting requirements. I present results from a complemented roving/mail-in survey undertaken during 2013-2014 to estimate recreational effort and catch of Atlantic salmon (Salmo salar) and brown trout (S. trutta) in the largest lake in the European Union, Lake Vänern, Sweden. I also evaluate different angler catch reporting methods (mail-in, tournament reports and face-to-face interviews) and compare catch rates within and among spring and fall fishing periods. In addition, mail-in survey data are examined for recall bias.   I estimate that 28.7 tonnes of salmon and trout combined were harvested by the recreational trolling fishery in 2014, more than the commercial and subsistence fisheries combined. Seasonal differences in both recreational effort and catch were observed. Effort, in boat hours, was significantly higher in spring than in fall. Catch rates of trout were higher in fall than in spring, but there were no seasonal differences in catches of salmon. Harvest per boat day did not differ significantly among catch reporting methods, indicating that all three methods could be useful for managers interested in harvest rates. In contrast, total and released catch per boat day differed among reporting methods, with tournament anglers catching more fish in total. Finally, there was little evidence for recall bias in mail-in surveys, indicating that mail-in surveys are useful for collecting unbiased catch data. My study is the most comprehensive angler survey to date for Lake Vänern, and my results should be of immediate use to local fisheries managers and should also be of interest to researchers and managers interested in estimating catch and effort for fisheries at large spatial scales.

Recruitment patterns and processes of coastal fish species in a temperate rocky reef

Tese de Doutoramento, Ciências do Mar, da Terra e do Ambiente, Ramo: Ciências do Mar, Especialização em Ecologia Marinha, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2016

APPLICATION OF AN IMPUTATION METHOD FOR GEOSPATIAL INVENTORY OF FOREST STRUCTURAL ATTRIBUTES ACROSS MULTIPLE SPATIAL SCALES IN THE LAKE STATES, U.S.A.

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.

Integration of a road traffic noise model (ASJ) and traffic simulation (AVENUE) for a built-up area

A road traffic noise prediction model (ASJ MODEL-1998) has been integrated with a road traffic simulator (AVENUE) to produce the Dynamic areawide Road traffic NoisE simulator-DRONE. This traffic-noise-GIS based integrated tool is upgraded to predict noise levels in built-up areas. The integration of traffic simulation with a noise model provides dynamic access to traffic flow characteristics and hence automated and detailed predictions of traffic noise. The prediction is not only on the spatial scale but also on temporal scale. The linkage with GIS gives a visual representation to noise pollution in the form of dynamic areawide traffic noise contour maps. The application of DRONE on a real world built-up area is also presented.

Intrinsic kinetics of titania photocatalysis : simplified models for their investigation

Even though titanium dioxide photocatalysis has been promoted as a leading green technology for water purification, many issues have hindered its application on a large commercial scale. For the materials scientist the main issues have centred the synthesis of more efficient materials and the investigation of degradation mechanisms; whereas for the engineers the main issues have been the development of appropriate models and the evaluation of intrinsic kinetics parameters that allow the scale up or re-design of efficient large-scale photocatalytic reactors. In order to obtain intrinsic kinetics parameters the reaction must be analysed and modelled considering the influence of the radiation field, pollutant concentrations and fluid dynamics. In this way, the obtained kinetic parameters are independent of the reactor size and configuration and can be subsequently used for scale-up purposes or for the development of entirely new reactor designs. This work investigates the intrinsic kinetics of phenol degradation over titania film due to the practicality of a fixed film configuration over a slurry. A flat plate reactor was designed in order to be able to control reaction parameters that include the UV irradiance, flow rates, pollutant concentration and temperature. Particular attention was paid to the investigation of the radiation field over the reactive surface and to the issue of mass transfer limited reactions. The ability of different emission models to describe the radiation field was investigated and compared to actinometric measurements. The RAD-LSI model was found to give the best predictions over the conditions tested. Mass transfer issues often limit fixed film reactors. The influence of this phenomenon was investigated with specifically planned sets of benzoic acid experiments and with the adoption of the stagnant film model. The phenol mass transfer coefficient in the system was calculated to be km,phenol=8.5815x10-7Re0.65(ms-1). The data obtained from a wide range of experimental conditions, together with an appropriate model of the system, has enabled determination of intrinsic kinetic parameters. The experiments were performed in four different irradiation levels (70.7, 57.9, 37.1 and 20.4 W m-2) and combined with three different initial phenol concentrations (20, 40 and 80 ppm) to give a wide range of final pollutant conversions (from 22% to 85%). The simple model adopted was able to fit the wide range of conditions with only four kinetic parameters; two reaction rate constants (one for phenol and one for the family of intermediates) and their corresponding adsorption constants. The intrinsic kinetic parameters values were defined as kph = 0.5226 mmol m-1 s-1 W-1, kI = 0.120 mmol m-1 s-1 W-1, Kph = 8.5 x 10-4 m3 mmol-1 and KI = 2.2 x 10-3 m3 mmol-1. The flat plate reactor allowed the investigation of the reaction under two different light configurations; liquid and substrate side illumination. The latter of particular interest for real world applications where light absorption due to turbidity and pollutants contained in the water stream to be treated could represent a significant issue. The two light configurations allowed the investigation of the effects of film thickness and the determination of the catalyst optimal thickness. The experimental investigation confirmed the predictions of a porous medium model developed to investigate the influence of diffusion, advection and photocatalytic phenomena inside the porous titania film, with the optimal thickness value individuated at 5 ìm. The model used the intrinsic kinetic parameters obtained from the flat plate reactor to predict the influence of thickness and transport phenomena on the final observed phenol conversion without using any correction factor; the excellent match between predictions and experimental results provided further proof of the quality of the parameters obtained with the proposed method.

A toolbox for animal call recognition

Monitoring the natural environment is increasingly important as habit degradation and climate change reduce theworld’s biodiversity.We have developed software tools and applications to assist ecologists with the collection and analysis of acoustic data at large spatial and temporal scales.One of our key objectives is automated animal call recognition, and our approach has three novel attributes. First, we work with raw environmental audio, contaminated by noise and artefacts and containing calls that vary greatly in volume depending on the animal’s proximity to the microphone. Second, initial experimentation suggested that no single recognizer could dealwith the enormous variety of calls. Therefore, we developed a toolbox of generic recognizers to extract invariant features for each call type. Third, many species are cryptic and offer little data with which to train a recognizer. Many popular machine learning methods require large volumes of training and validation data and considerable time and expertise to prepare. Consequently we adopt bootstrap techniques that can be initiated with little data and refined subsequently. In this paper, we describe our recognition tools and present results for real ecological problems.

Sampling environmental acoustic recordings to determine species richness : I

Acoustic sensors provide an effective means of monitoring biodiversity at large spatial and temporal scales. They can continuously and passively record large volumes of data over extended periods, however these data must be analysed to detect the presence of vocal species. Automated analysis of acoustic data for large numbers of species is complex and can be subject to high levels of false positive and false negative results. Manual analysis by experienced users can produce accurate results, however the time and effort required to process even small volumes of data can make manual analysis prohibitive. Our research examined the use of sampling methods to reduce the cost of analysing large volumes of acoustic sensor data, while retaining high levels of species detection accuracy. Utilising five days of manually analysed acoustic sensor data from four sites, we examined a range of sampling rates and methods including random, stratified and biologically informed. Our findings indicate that randomly selecting 120, one-minute samples from the three hours immediately following dawn provided the most effective sampling method. This method detected, on average 62% of total species after 120 one-minute samples were analysed, compared to 34% of total species from traditional point counts. Our results demonstrate that targeted sampling methods can provide an effective means for analysing large volumes of acoustic sensor data efficiently and accurately.