Search

Background The COVID-19 pandemic imposes major challenges for care of cancer patients. Objectives Our aim was to assess the effects of the pandemic on treatment and appointments of patients with malignant melanoma based on a large skin cancer centre in Berlin, Germany, and identify reasons for, and impact factors associated with these changes. Materials & Methods Patients with melanoma treated from January 1st 2019 received a postal survey with questions on impairment due to the pandemic, fear of COVID-19, fear of melanoma, changes in therapy and/or appointments, including reasons for the changes. Impact factors on postponed/missed appointments were examined using descriptive analyses and multivariate logistic regression. Results The response rate was 41.3% (n = 324; 57.4% males; mean age: 67.9 years). Among 104 participants currently receiving therapy, four (3.8%) reported treatment changes due to the pandemic. Postponements or cancellations of appointments occurred in 48 participants (14.8%), most frequently, at their own request (81.3%) due to fear of SARS-CoV-2 infection (68.8%). Current treatment was associated with a reduced chance of postponing/missing appointments (OR = 0.208, p = 0.003), whereas a high or very high level of concern for COVID-19 (OR = 6.806, p = 0.034; OR = 10.097, p = 0.038), SARS-CoV-2 infection among close acquaintances (OR = 4.251, p = 0.026), anxiety disorder (OR = 5.465, p = 0.016) and AJCC stage IV (OR = 3.108, p = 0.048) were associated with a higher likelihood of postponing/missing appointments. Conclusion Among our participants, treatment changes were rare and the proportion of missed/delayed appointments was rather small. The main reasons for delays/cancellations of appointments were anxiety and concern for COVID-19. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1684/ejd.2021.4098. Table S1. Disease and treatment characteristics and their association with postponed/missed appointments. Table S2. Treatment changes or postponement due to the pandemic. Table S3. Comorbidities and their association with postponed/missed appointments. Fig. S1. Number of new SARS-CoV-2 infections per day in Germany and Berlin between February 1st 2020 and July 1st 2020.

Background The 150 grassland plots were located in three study regions in Germany, 50 in each region. The dataset describes the yearly grassland management for each grassland plot using 116 variables. General information includes plot identifier, study region and survey year. Additionally, grassland plot characteristics describe the presence and starting year of drainage and whether arable farming had taken place 25 years before our assessment, i.e. between 1981 and 2006. In each year, the size of the management unit is given which, in some cases, changed slightly across years. Mowing, grazing and fertilisation were systematically surveyed: Mowing is characterised by mowing frequency (i.e. number of cuts per year), dates of cutting and different technical variables, such as type of machine used or usage of conditioner. For grazing, the livestock species and age (e.g. cattle, horse, sheep), the number of animals, stocking density per hectare and total duration of grazing were recorded. As a derived variable, the mean grazing intensity was then calculated by multiplying the livestock units with the duration of grazing per hectare [LSU days/ha]. Different grazing periods during a year, partly involving different herds, were summed up to an annual grazing intensity for each grassland. For fertilisation, information on the type and amount of different types of fertilisers was recorded separately for mineral and organic fertilisers, such as solid farmland manure, slurry and mash from a bioethanol factory. Our fertilisation measures neglect dung dropped by livestock during grazing. For each type of fertiliser, we calculated its total nitrogen content, derived from chemical analyses by the producer or agricultural guidelines (Table 3). All three management types, mowing, fertilisation and grazing, were used to calculate a combined land use intensity index (LUI) which is frequently used to define a measure for the land use intensity. Here, fertilisation is expressed as total nitrogen per hectare [kg N/ha], but does not consider potassium and phosphorus. Information on additional management practices in grasslands was also recorded including levelling, to tear-up matted grass covers, rolling, to remove surface irregularities, seed addition, to close gaps in the sward., Biodiversity Data Journal, 7

Habitat and species-distribution models are important tools in ecology, conservation and wildlife management. For the modelling of the species’ habitat on small spatial scales, the availability of suitable habitat variables is crucial. Collecting data in the field often incurs considerable costs. Forest agencies, however, collect data on forest structure and composition on a large extent and with a fine grain (200 m ×200 m) during their forest inventories. Despite this the potential use of these data for habitat modelling and therefore conservation and wildlife management is still broadly neglected. Here, we evaluated and compared the performance of models based on environmental data derived purely from forest inventories, inventory data plus additional ecological information gained during the inventory, and aerial photographs to model the habitat of a forest species, the capercaillie (Tetrao urogallus L.). The best models developed from each of the three data sources showed a similar performance over the area (28 450 ha) and the considered grain (50 ha grids); all models had an area under the receiver operating characteristic curve (AUC) value . 0.85, and various other metrics used for evaluating the models showed little differences. In all calibration plots, the predicted probabilities of presence were very similar to that of the observed probabilities, which indicated that our maps of the habitat suitability are a good representation of the real occurrences. This modelling of the capercaillie’s habitat using existing forest inventory data was successful because the variables collected in standard inventories also have some meaning for the ecology of the species. To model the habitat of forest species that need special resources outside the scope of forestry, possible additional variables could be sampled at low extra costs during such large inventories. There is therefore the potential for forest inventories, currently undertaken to support commercial forestry, to be applied in conservation, thereby supporting a multifunctional forestry.