Saproxylic beetles respond to habitat variables at different spatial scales depending on variable type and species’ mobility: the need for multi-scale forest structure management

The response of species to the environment is scale-dependent and the spatial scale at which this relationships are measured may affect conservation recommendations. Saproxylic beetles depend on decaying- and deadwood which occur in lower quantities in managed compared to natural forests. Most studies have investigated the habitat selection of saproxylic beetles at the stand scale, however depending on the species mobility, the amounts and distribution of forest attributes across the landscape may be equally important, and thus crucial to frame quantitative conservation targets. To address this gap, we evaluated the influence of environmental variables, derived from remote sensing across multiple spatial scales (50, 100, 250, 500 and 1000 m radius), on saproxylic beetles habitat selection. Focusing on four mobile and four flightless species, we hypothesized that mobile species respond to habitat variables at broader scales compared to flightless species, and that variables describing forest structure explain species presence better at smaller scales than variables describing other landscape features. Forest structure variables explained around 40% of the habitat selection, followed by variables describing forest type, topography and climate. Contrary to our expectations, mobile species responded to variables at smaller scales than flightless species. Saproxylic beetle species therefore respond to the availability of habitat features at spatial scales that are inversely related to their dispersal capacities, suggesting that less mobile species require larger areas with suitable habitat characteristics while mobile species can also make use of small, distributed patches with locally concentrated habitat features

Automated Detection of Forest Gaps in Spruce Dominated Stands Using Canopy Height Models Derived from Stereo Aerial Imagery

Forest gaps are important structural elements in forest ecology to which various conservation-relevant, photophilic species are associated. To automatically map forest gaps and detect their changes over time, we developed a method based on Digital Surface Models (DSM) derived from stereoscopic aerial imagery and a LiDAR-based Digital Elevation Model (LiDAR DEM). Gaps were detected and delineated in relation to height and cover of the surrounding forest comparing data from two public flight campaigns (2009 and 2012) in a 1023-ha model region in the Northern Black Forest, Southwest Germany. The method was evaluated using an independent validation dataset obtained by visual stereo-interpretation. Gaps were automatically detected with an overall accuracy of 0.90 (2009) and 0.82 (2012). However, a very high producers’ accuracy of more than 0.95 (both years) was counterbalanced by a user’s accuracy of 0.84 (2009) and 0.73 (2012) as some gaps were not automatically detected. Accuracy was mainly dependent on the shadow occurrence and height of the surrounding forest with user’s accuracies dropping to 0.70 (2009) and 0.52 (2012) in high stands (>8 m tree height). As one important step in the workflow, the class of open forest, an important feature for many forest species, was delineated with a very good overall accuracy of 0.92 (both years) with uncertainties occurring mostly in areas with intermediate canopy cover. Presence of complete or partial shadow and geometric limitations of stereo image matching were identified as the main sources of errors in the method performance, suggesting that images with a higher overlap and resolution and ameliorated image-matching algorithms provide the greatest potential for improvement

Sublethal necroptosis signaling promotes inflammation and liver cancer

It is currently not well known how necroptosis and necroptosis responses manifest in vivo. Here, we uncovered a molecular switch facilitating reprogramming between two alternative modes of necroptosis signaling in hepatocytes, fundamentally affecting immune responses and hepatocarcinogenesis. Concomitant necrosome and NF-κB activation in hepatocytes, which physiologically express low concentrations of receptor-interacting kinase 3 (RIPK3), did not lead to immediate cell death but forced them into a prolonged "sublethal" state with leaky membranes, functioning as secretory cells that released specific chemokines including CCL20 and MCP-1. This triggered hepatic cell proliferation as well as activation of procarcinogenic monocyte-derived macrophage cell clusters, contributing to hepatocarcinogenesis. In contrast, necrosome activation in hepatocytes with inactive NF-κB-signaling caused an accelerated execution of necroptosis, limiting alarmin release, and thereby preventing inflammation and hepatocarcinogenesis. Consistently, intratumoral NF-κB-necroptosis signatures were associated with poor prognosis in human hepatocarcinogenesis. Therefore, pharmacological reprogramming between these distinct forms of necroptosis may represent a promising strategy against hepatocellular carcinoma

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.Comment: Shared first authors: Annika Reinke, Minu D. Tizabi; shared senior authors: Paul F. J\"ager, Lena Maier-Hei

Bat habitat selection reveals positive effects of retention forestry

Retention forestry, which retains small set-asides within forests managed for timber production and other services, is an important conservation instrument for enhancing structural complexity and biodiversity in multifunctional forests. However, in contrast to local scale effects, its large-scale effectiveness is largely unknown, as this requires area-wide and sufficiently precise information on key structural elements and associated species’ habitats. Bats are particularly sensitive to forest structural characteristics and are target organisms of most retention programs. To assess their response to existing retention efforts, we here compared key habitat structures and overall habitat suitability for bats across forest areas with and without retention, using forest type and structure variables derived from remote sensing along with topographic, climatic and land-cover variables in a multi-scale modelling approach. Based on acoustic data from 135 1-hectare plots across the Black Forest, Germany, we calibrated region-wide species distribution models for 9 bat species or bat species groups thereby identifying the best-performing scale (50 – 1000 m radius) for each predictor and species(-group). Among predictors and species(-groups), forest cover and structural variables explained most (44.0 % and 38.3 %) of bat habitat selection, with forest height heterogeneity (16.4 %) and the percentage area with standing dead trees (11.7 %) performing best, mostly at small scales (50–100 m). Forests with retention showed higher values of these key structural variables, resulting in higher predicted habitat suitability for all species(-groups), highlighting positive effects of retention on structural complexity in forests and on species that benefit thereof

Remotely Sensed Single Tree Data Enable the Determination of Habitat Thresholds for the Three-Toed Woodpecker (<i>Picoides tridactylus</i>)

Forest biodiversity conservation requires precise, area-wide information on the abundance and distribution of key habitat structures at multiple spatial scales. We combined airborne laser scanning (ALS) data with color-infrared (CIR) aerial imagery for identifying individual tree characteristics and quantifying multi-scale habitat requirements using the example of the three-toed woodpecker (Picoides tridactylus) (TTW) in the Bavarian Forest National Park (Germany). This bird, a keystone species of boreal and mountainous forests, is highly reliant on bark beetles dwelling in dead or dying trees. While previous studies showed a positive relationship between the TTW presence and the amount of deadwood as a limiting resource, we hypothesized a unimodal response with a negative effect of very high deadwood amounts and tested for effects of substrate quality. Based on 104 woodpecker presence or absence locations, habitat selection was modelled at four spatial scales reflecting different woodpecker home range sizes. The abundance of standing dead trees was the most important predictor, with an increase in the probability of TTW occurrence up to a threshold of 44&#8315;50 dead trees per hectare, followed by a decrease in the probability of occurrence. A positive relationship with the deadwood crown size indicated the importance of fresh deadwood. Remote sensing data allowed both an area-wide prediction of species occurrence and the derivation of ecological threshold values for deadwood quality and quantity for more informed conservation management

Detection of Standing Deadwood from Aerial Imagery Products: Two Methods for Addressing the Bare Ground Misclassification Issue

Deadwood mapping is of high relevance for studies on forest biodiversity, forest disturbance, and dynamics. As deadwood predominantly occurs in forests characterized by a high structural complexity and rugged terrain, the use of remote sensing offers numerous advantages over terrestrial inventory. However, deadwood misclassifications can occur in the presence of bare ground, displaying a similar spectral signature. In this study, we tested the potential to detect standing deadwood (h &gt; 5 m) using orthophotos (0.5 m resolution) and digital surface models (DSM) (1 m resolution), both derived from stereo aerial image matching (0.2 m resolution and 60%/30% overlap (end/side lap)). Models were calibrated in a 600 ha mountain forest area that was rich in deadwood in various stages of decay. We employed random forest (RF) classification, followed by two approaches for addressing the deadwood-bare ground misclassification issue: (1) post-processing, with a mean neighborhood filter for &ldquo;deadwood&rdquo;-pixels and filtering out isolated pixels and (2) a &ldquo;deadwood-uncertainty&rdquo; filter, quantifying the probability of a &ldquo;deadwood&rdquo;-pixel to be correctly classified as a function of the environmental and spectral conditions in its neighborhood. RF model validation based on data partitioning delivered high user&rsquo;s (UA) and producer&rsquo;s (PA) accuracies (both &gt; 0.9). Independent validation, however, revealed a high commission error for deadwood, mainly in areas with bare ground (UA = 0.60, PA = 0.87). Post-processing (1) and the application of the uncertainty filter (2) improved the distinction between deadwood and bare ground and led to a more balanced relation between UA and PA (UA of 0.69 and 0.74, PA of 0.79 and 0.80, under (1) and (2), respectively). Deadwood-pixels showed 90% location agreement with manually delineated reference to deadwood objects. With both alternative solutions, deadwood mapping achieved reliable results and the highest accuracies were obtained with deadwood-uncertainty filter. Since the information on surface heights was crucial for correct classification, enhancing DSM quality could substantially improve the results