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Reports of major losses in insect biodiversity have stimulated an increasing interest in temporal population changes. Existing datasets are often limited to a small number of study sites, few points in time, a narrow range of land‐use intensities and only some taxonomic groups, or they lack standardised sampling. While new monitoring programs have been initiated, they still cover rather short time periods.
Daskalova et al. 2021 (Insect Conservation and Diversity, 14, 1‐18) argue that temporal trends of insect populations derived from short time series are biased towards extreme trends, while their own analysis of an assembly of shorter‐ and longer‐term time series does not support an overall insect decline. With respect to the results of Seibold et al. 2019 (Nature, 574, 671–674) based on a 10‐year multi‐site time series, they claim that the analysis suffers from not accounting for temporal pseudoreplication.
Here, we explain why the criticism of missing statistical rigour in the analysis of Seibold et al. (2019) is not warranted. Models that include ‘year’ as random effect, as suggested by Daskalova et al. (2021), fail to detect non‐linear trends and assume that consecutive years are independent samples which is questionable for insect time‐series data.
We agree with Daskalova et al. (2021) that the assembly and analysis of larger datasets is urgently needed, but it will take time until such datasets are available. Thus, short‐term datasets are highly valuable, should be extended and analysed continually to provide a more detailed understanding of insect population changes under the influence of global change, and to trigger immediate conservation actions.
The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results.
Background
Despite advances in treatment of patients with non-small cell lung cancer, carriers of certain genetic alterations are prone to failure. One such factor frequently mutated, is the tumor suppressor PTEN. These tumors are supposed to be more resistant to radiation, chemo- and immunotherapy.
Results
We demonstrate that loss of PTEN led to altered expression of transcriptional programs which directly regulate therapy resistance, resulting in establishment of radiation resistance. While PTEN-deficient tumor cells were not dependent on DNA-PK for IR resistance nor activated ATR during IR, they showed a significant dependence for the DNA damage kinase ATM. Pharmacologic inhibition of ATM, via KU-60019 and AZD1390 at non-toxic doses, restored and even synergized with IR in PTEN-deficient human and murine NSCLC cells as well in a multicellular organotypic ex vivo tumor model.
Conclusion
PTEN tumors are addicted to ATM to detect and repair radiation induced DNA damage. This creates an exploitable bottleneck. At least in cellulo and ex vivo we show that low concentration of ATM inhibitor is able to synergise with IR to treat PTEN-deficient tumors in genetically well-defined IR resistant lung cancer models.
The transcription factor ∆Np63 is a master regulator of epithelial cell identity and essential for the survival of squamous cell carcinoma (SCC) of lung, head and neck, oesophagus, cervix and skin. Here, we report that the deubiquitylase USP28 stabilizes ∆Np63 and maintains elevated ∆NP63 levels in SCC by counteracting its proteasome‐mediated degradation. Impaired USP28 activity, either genetically or pharmacologically, abrogates the transcriptional identity and suppresses growth and survival of human SCC cells. CRISPR/Cas9‐engineered in vivo mouse models establish that endogenous USP28 is strictly required for both induction and maintenance of lung SCC. Our data strongly suggest that targeting ∆Np63 abundance via inhibition of USP28 is a promising strategy for the treatment of SCC tumours.
Species' functional traits set the blueprint for pair-wise interactions in ecological networks. Yet, it is unknown to what extent the functional diversity of plant and animal communities controls network assembly along environmental gradients in real-world ecosystems. Here we address this question with a unique dataset of mutualistic bird-fruit, bird-flower and insect-flower interaction networks and associated functional traits of 200 plant and 282 animal species sampled along broad climate and land-use gradients on Mt. Kilimanjaro. We show that plant functional diversity is mainly limited by precipitation, while animal functional diversity is primarily limited by temperature. Furthermore, shifts in plant and animal functional diversity along the elevational gradient control the niche breadth and partitioning of the respective other trophic level. These findings reveal that climatic constraints on the functional diversity of either plants or animals determine the relative importance of bottom-up and top-down control in plant-animal interaction networks.