Det sker stora förändringar i de regional likt globala fågelbestånden. I Sverige sker både ökningar och minskningar av populationerna, vilket orsakas av flera olika faktorer. En av dessa är tillgången på föda. Därför är syftet med denna studie att undersöka om avslutad fågelmatning någon påverkan på ett lokalt fågelbestånd.Fågelsamhället på Fredriksdals museer och trädgårdar i Helsingborg studerades medstandardiserade revirkarteringar med en anpassad linjekartering av området under perioden 2017–2022. De faktorer som undersöks är förhållandet mellan de arter som förekommer vid matning eller ej, förhållandet mellan stann- och flyttfåglar samt utvecklingen av det totala antalet arter och par under undersökningsperioden. Resultatet visar att det totala antalet fåglar på Fredriksdal minskat med 50% under de senaste sex åren medan antalet arter varit stabilt. Det observerades ingen signifikant skillnad i minskningen av de fåglar som förekommer vid matning och gruppen som inte förekommer vid matningen. Det fanns inte heller någon signifikant skillnad mellan minskningen av de övervintrande fåglarna och flyttfåglarna. Dessa båda grupper visade en stabil negativ trend över undersökningsperioden. Detta betyder att den upphörda fågelmatningen kan ha påverkat bestånden negativt men det är isfallbara för enskilda arter och det kan inte förklara den totala minskningen. Några delförklaringar till den negativa trenden är klimatförändringarna som på global nivå påverkar fågelbestånden, förändringar i habitat både på Fredriksdal och på flyttfåglarnas övervintringslokaler och minskad tillgång på föda främst på grund av det minskande antalet insekter i hela Europa. / There are major changes in regional as well as global bird populations. In Sweden, there are both increases and decreases in populations, which is caused by several different factors. One of these is the availability of food. Therefore, the purpose of thisstudy is to investigate whether completed bird feeding at Fredriksdal's museums and gardens in Helsingborg has any impact on the local bird population. The method used is standardized area mapping with an adapted line mapping of the area during the period 2017–2022. The factors investigated are the ratio between the species that occur when feeding or not, the ratio between resident and migratory birds and the development of the total number of species and pairs during the study period. The result shows that the total number of birds at Fredriksdal has decreased by 50% over the past six years, while the number of species has remained stable. No significant difference was observed in the reduction of the birds present at feeding and the group not present at feeding. There was also no significant difference between the decline of wintering birds and migratory birds. These two groups showed a stable negative trend over the survey period. This means that the cessation of bird feeding may have had a negative effect on the populations, but it is icefall only for individual species and it cannot explain the overall decline. Some partial explanations for the negative trend are the climate changes that affect bird populations on a global level, changes in habitat both at Fredriksdal and at the migratory birds' wintering grounds as well as reduced availability of food mainly due to the decreasing number of insects throughout Europé.

Der Atlas behandelt 213 aktuelle und ehemalige Brutvogelarten in Sachsen. Hiervon werden 177 Arten ausführlich mit den Schwerpunkten Verbreitung, Lebensraum, Brutbestand, Phänologie und Brutbiologie sowie Gefährdung und Schutz besprochen. Aus drei Zeitebenen liegen landesweite Bearbeitungen der Brutvogelfauna auf Rasterbasis vor. Die Ergebnisse ermöglichten es, in detaillierten Karten Bestandstrends darzustellen und Veränderungen von Verbreitung und Häufigkeit nachzuvollziehen. 293 Fotos veranschaulichen die Vielfalt der Vogelarten und zeigen ihren typischen Lebensraum.

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Multiple studies have documented declines of forest breeding birds in the eastern United States, but the temporal and spatial scales of most studies limit inference regarding large scale bird-habitat trends. A potential solution to this challenge is integrating existing long-term datasets such as the U.S. Forest Service Forest Inventory and Analysis (FIA) program and U.S. Geological Survey Breeding Bird Survey (BBS) that span large geographic regions. The purposes of this study were to determine if FIA metrics can be related to BBS population indices at multiple spatial and temporal scales and to develop predictive models from these relationships that identify forest conditions favorable to forest songbirds. I accumulated annual route-level BBS data for 4 species guilds (canopy nesting, ground and shrub nesting, cavity nesting, early successional), each containing a minimum of five bird species, from 1966-2004. I developed 41 forest variables describing forest structure at the county level using FIA data from for the 2000 inventory cycle within 5 physiographic regions in 14 states (AL, GA, IL, IN, KY, MD, NC, NY, OH, PA, SC, TN, VA, and WV). I examine spatial relationships between the BBS and FIA data at 3 hierarchical scales: 1) individual BBS routes, 2) FIA units, and 3) and physiographic sections. At the BBS route scale, I buffered each BBS route with a 100m, 1km, and 10km buffer, intersected these buffers with the county boundaries, and developed a weighted average for each forest variable within each buffer, with the weight being a function of the percent of area each county had within a given buffer. I calculated 28 variables describing landscape structure from 1992 NLCD imagery using Fragstats within each buffer size. I developed predictive models relating spatial variations in bird occupancy and abundance to changes in forest and landscape structure using logistic regression and classification and regression trees (CART). Models were developed for each of the 3 buffer sizes, and I pooled the variables selected for the individual models and used them to develop multiscale models with the BBS route still serving as the sample unit. At the FIA unit and physiographic section scales I calculated average abundance/route for each bird species within each FIA unit and physiographic section and extrapolated the plot-level FIA variables to the FIA unit and physiographic section levels. Landscape variables were recalculated within each unit and section using NCLD imagery resampled to a 400 m pixel size. I used regression trees (FIA unit scale) and general linear models (GLM, physiographic section scale) to relate spatial variations in bird abundance to the forest and landscape variables. I examined temporal relationships between the BBS and FIA data between 1966 and 2000. I developed 13 forest variables from statistical summary reports for 4 FIA inventory cycles (1965, 1975, 1989, and 2000) within NY, PA, MD, and WV. I used linear interpolation to estimate annual values of each FIA variable between successive inventory cycles and GLMs to relate annual variations in bird abundance to the forest variables. At the BBS route scale, the CART models accounted for > 50% of the variation in bird presence-absence and abundance. The logistic regression models had sensitivity and specificity rates > 0.50. By incorporating the variables selected for the models developed within each buffer (100m, 1km, and 10km) around the BBS routes into a multiscale model, I was able to further improve the performance of many of the models and gain additional insight regarding the contribution of multiscale influences on bird-habitat relationships. The majority of the best CART models tended to be the multiscale models, and many of the multiscale logistic models had greater sensitivity and specificity than their single-scale counter parts. The relatively fine resolution and extensive coverage of the BBS, FIA, and NLCD datasets coupled with the overlapping multiscale approach of these analyses allowed me to incorporate levels of variation in both habitat and bird occurrence and abundance into my models that likely represented a more comprehensive range of ecological variability in the bird-habitat relationships relative to studies conducted at smaller scales and/or using data at coarser resolutions. At the FIA unit and physiographic section scales, the regression trees accounted for an average of 54.1% of the variability in bird abundance among FIA units, and the GLMs accounted for an average of 66.3% of the variability among physiographic sections. However, increasing the observational and analytical scale to the FIA unit and physiographic section decreased the measurement resolution of the bird abundance and landscape variables. This limits the applicability and interpretive strength of the models developed at these scales, but they may serve as indices to those habitat components exerting the greatest influences on bird abundance at these broader scales. The GLMs relating average annual bird abundance to annual estimates of forest variables developed using statistical report data from the 1965, 1975, 1989, and 2000 FIA inventories explained an average of 62.0% of the variability in annual bird abundance estimates. However, these relationships were a function of both the general habitat characteristics and the trends in bird abundance specific to the 4-state region (MD, NY, PA, and WV) used for these analyses and may not be applicable to other states or regions. The small suite of variables available from the FIA statistical reports and multicollinearity among all forest variables further limited the applicability of these models. As with those developed at the FIA unit and physiographic sections scales, these models may serve as general indices to the habitat components exerting the greatest influences on bird abundance trends through time at regional scales. These results demonstrate that forest variables developed from the FIA, in conjunction with landscape variables, can explain variations in occupancy and abundance estimated from BBS data for forest bird species with a variety of habitat requirements across spatial and temporal scales. / Ph. D.

The global loss of biodiversity calls for robust large-scale diversity assessment. Biological diversity is a multi-faceted concept; defined as the “variety of life”, answering questions such as “How much is there?” or more precisely “Have we succeeded in reducing the rate of its decline?” is not straightforward. While various aspects of biodiversity give rise to numerous ways of quantification, we focus on temporal (and spatial) trends and their changes in species diversity. Traditional diversity indices summarise information contained in the species abundance distribution, i.e. each species' proportional contribution to total abundance. Estimated from data, these indices can be biased if variation in detection probability is ignored. We discuss differences between diversity indices and demonstrate possible adjustments for detectability. Additionally, most indices focus on the most abundant species in ecological communities. We introduce a new set of diversity measures, based on a family of goodness-of-fit statistics. A function of a free parameter, this family allows us to vary the sensitivity of these measures to dominance and rarity of species. Their performance is studied by assessing temporal trends in diversity for five communities of British breeding birds based on 14 years of survey data, where they are applied alongside the current headline index, a geometric mean of relative abundances. Revealing the contributions of both rare and common species to biodiversity trends, these "goodness-of-fit" measures provide novel insights into how ecological communities change over time. Biodiversity is not only subject to temporal changes, but it also varies across space. We take first steps towards estimating spatial diversity trends. Finally, processes maintaining biodiversity act locally, at specific spatial scales. Contrary to abundance-based summary statistics, spatial characteristics of ecological communities may distinguish these processes. We suggest a generalisation to a spatial summary, the cross-pair overlap distribution, to render it more flexible to spatial scale.