Lemon, L. | bioRxiv Science

bioRxiv2025

Drivers of individual plant species contributions to β-diversity are scale-dependent

Species introductions and native extirpations are driving biotic homogenisation in plant communities by reducing {beta}-diversity. Individual species vary in their contributions to {beta}-diversity (Species Contribution to {beta}-Diversity; species-{beta}), yet our understanding of how species characteristics shape these contributions remains limited. Additionally, although the ecological processes influencing {beta}-diversity are known to vary with spatial scale, we lack understanding of how species contributions, or their underlying determinants, change across scales. Here, we modelled how plant functional traits, phylogenetic relatedness, and introduction status influence their contributions to {beta}-diversity using plant community data from 429 plots surveyed from 2017-2023 across three nested spatial scales (up to 1 km2) in nine sites spanning four countries. We extended the analysis to broader spatial extents (100 km2 to the entire UK) using GBIF occurrence data. We found that functional traits associated with competitive ability influenced species-{beta}, but the direction and strength of their effects varied with scale. Likewise, phylogenetic novelty increased species-{beta} at small scales but reduced it at larger ones. After accounting for traits and phylogeny, introduced species consistently contributed less to {beta}-diversity than native species--especially at broader spatial extents. These results demonstrate that species ecological and evolutionary characteristics shape their contributions to {beta}-diversity, but that these effects are scale-dependent. Our findings highlight the importance of scale-explicit approaches in understanding both the determinants of {beta}-diversity, and how we can combat its loss to mitigate biotic homogenisation.

ecology↗

bioRxiv2019

Molecular and cellular phenotypic differences distinguish murine syngeneic models from human tumors

AbstractThe clinical success of immune checkpoint inhibitors that target cytotoxic T-lymphocyte associated protein 4 (CTLA4) and programmed cell death protein 1 (PD-1) or programmed death ligand-1 (PD-L1) demonstrates that reactivation of the human immune system delivers durable responses for some patients and represents an exciting approach for cancer treatment. The combination of multiple immunotherapies as well as the combination of immunotherapy with targeted therapy is being pursued vigorously to increase the rate and extend the duration of response. Preclinical in vivo models for immuno-oncology (IO) typically require immunocompetent mice bearing murine syngeneic tumors. To facilitate translation of preclinical studies into human, we characterized the genomic, transcriptomic, and protein expression of a panel of mouse tumor cell lines grown in vitro culture as well as in vivo tumor samples. Our studies identified many genetic and cellular phenotypic differences that distinguish murine syngeneic models from human cancers. For example, only a small fraction of the somatic single nucleotide variants (SNVs) in mouse cell lines directly match SNVs from human actionable cancer genes. At the cellular level, some epithelial tumor models have a more mesenchymal phenotype with relatively low T-lymphocyte infiltration compared to the corresponding human cancers. Furthermore, in contrast to what has been reported for human tumors, we did not observe a correlation between neoantigen load and cytolytic activity in syngeneic models. Finally, the relative immunogenicity of syngeneic tumors does not typically resemble that of human tumors of the same tissue origin. CT26, a colon tumor model, had the highest immunogenicity and was the most responsive model to CTLA4 inhibitor treatment, by contrast to the relatively low immunogenicity and response rate to checkpoint inhibitor therapies in human colon cancers. These differences highlight limitations of syngeneic models for evaluating novel immune therapies and rationalize some of the challenges associated with translating preclinical findings to clinical studies.

cancer biology↗