Pathological Findings in Eurasian Otters (Lutra lutra) Found Dead between 2015−2020 in Schleswig-Holstein, Germany

In times of massive biodiversity loss and ongoing environmental crises, it is extremely important to ensure long-term conservation efforts of threatened species like Eurasian otters (Lutra lutra). To gain insights into the status of Northern Germany’s otter population, 92 otters found dead in Schleswig-Holstein between 2015−2020 were collected and underwent detailed dissection with the aim to establish a monitoring program for this population. Examinations followed a protocol especially designed for otters, including various biological data assessments and extended sampling. The finding sites showed a clear concentration in the Continental region. Seasonal concentration differed among the years, yet peaks were seen from fall to winter. Overall, more males than females were found, although this differed among the years. The majority of otters that could be aged were between 1–3 years. Placental scars and pregnancy were recorded in only few females. Nutritional status was good in most cases. Infectious diseases found included Vagococcus lutrae, Toxoplasma gondii, and Emmonsia spp. A major cause of death was roadkill. Known sample bias in studies focusing on roadkill was considered in the finding interpretation. Continuation of the population health investigations is mandatory to analyze potential trends and to establish an actual monitoring program for Eurasian otters in Schleswig-Holstein.

Adversarial domain adaptation to reduce sample bias of a high energy physics classifier

We apply adversarial domain adaptation to reduce sample bias in a classification machine learning algorithm. We add a gradient reversal layer to a neural network to simultaneously classify signal versus background events, while minimising the difference of the classifier response to a background sample using an alternative MC model. We show this on the example of simulated events at the LHC with $t\bar{t}H$ signal versus $t\bar{t}b\bar{b}$ background classification.

An ALMA study of hub-filament systems I. On the clump mass concentration within the most massive cores

The physical processes behind the transfer of mass from parsec-scale clumps to massive-star-forming cores remain elusive. We investigate the relation between the clump morphology and the mass fraction that ends up in its most massive core (MMC) as a function of infrared brightness, i.e. a clump evolutionary tracer. Using ALMA 12 m and ACA we surveyed 6 infrared-dark hubs in 2.9 mm continuum at ∼3″ resolution. To put our sample into context, we also re-analysed published ALMA data from a sample of 29 high mass-surface density ATLASGAL sources. We characterise the size, mass, morphology, and infrared brightness of the clumps using Herschel and Spitzer data. Within the 6 newly observed hubs, we identify 67 cores, and find that the MMCs have masses between 15–911 M⊙ within a radius of 0.018–0.156 pc. The MMC of each hub contains 3–24 per cent of the clump mass (fMMC), becoming 5–36 per cent once core masses are normalised to the median core radius. Across the 35 clumps, we find no significant difference in the median fMMC values of hub and non-hub systems, likely the consequence of a sample bias. However, we find that fMMC is ∼7.9 times larger for infrared-dark clumps compared to infrared-bright ones. This factor increases up to ∼14.5 when comparing our sample of 6 infrared-dark hubs to infrared-bright clumps. We speculate that hub-filament systems efficiently concentrate mass within their MMC early on during its evolution. As clumps evolve, they grow in mass, but such growth does not lead to the formation of more massive MMCs.

Bias Mitigation in Data Sets

Tackling sample bias, Non-Response Bias, CognitiveBias, and disparate impact associated with Protected Categories in three parts/papers, data, algorithm construction, and output impact. This paper covers the Data section.

Here be dragons: important spatial uncertainty driven by climate data in forecasted distribution of an endangered insular reptile

The effect of future climate change is poorly documented in the tropics, especially in mountainous areas. Yet, species living in these environments are predicted to be strongly affected. Newly available high-resolution environmental data and statistical methods enable the development of forecasting models. Nevertheless, the uncertainty related to climate models can be strong, which can lead to ineffective conservation actions. Predicted studies aimed at providing conservation guidelines often account for a range of future climate predictions (climate scenarios and global circulation models). However, very few studies considered potential differences related to baseline climate data and/or did not account for spatial information (overlap) in uncertainty assessments. We modelled the environmental suitability for Phelsuma borbonica, an endangered reptile native to Reunion Island. Using two metrics of species range change (difference in overall suitability and spatial overlap), we quantified the uncertainty related to the modelling technique (n = 10), sample bias correction, climate change scenario, global circulation models (GCM) and baseline climate (CHELSA versus Worldclim). Uncertainty was mainly driven by GCMs when considering overall suitability, while for spatial overlap the uncertainty related to baseline climate became more important than that of GCMs. The uncertainty driven by sample bias correction and variable selection was much higher when assessed based on spatial overlap. The modelling technique was a strong driver of uncertainty in both cases. We eventually provide a consensus ensemble prediction map of the environmental suitability of P. borbonica to identify the areas predicted to be the most suitable in the future with the highest certainty. Predictive studies aimed at identifying priority areas for conservation in the face of climate change need to account for a wide panel of modelling techniques, GCMs and baseline climate data. We recommend the use of multiple approaches, including spatial overlap, when assessing uncertainty in species distribution models.

Fifty Shades of Terrorist Credit-Taking: A Review of the Existing Scholarship on Terrorist Credit-Taking

The consistently low credit-taking rates, 16 percent in 2016 (Global Terrorism Database), continue to challenge the understanding of terrorism as “propaganda of the deed” twenty years after researchers initially pointed out the conundrum. While providing an overview and evaluation of the limited existing literature on credit-taking, this paper draws attention to three core problems in the current literature on terrorist credit-taking, which may have led the low credit-taking rates to appear more puzzling than is due. First, the available explanations struggle to find corroboration when empirically tested and many also show theoretical shortcomings with strong unspoken assumptions and unclear predictions of credit-taking behavior. Second, data structure and data availability unnecessarily narrow our academic understanding of credit-taking. Finally, theories focused on group characteristics such as religious motivation or state sponsorship suffer from severe issues of sample bias due to unintended selection on the dependent variable. The paper concludes with suggestions for future research less prone to the problematic issues identified in the article. A re-orientation toward a less restrictive and more fine-grained understanding of credit-taking is advised.