Human appropriation of net primary production as driver of change in landscape-scale vertebrate richness

Aim

Land use is the most pervasive driver of biodiversity loss. Predicting its impact on species richness (SR) is often based on indicators of habitat loss. However, the degradation of habitats, especially through land-use intensification, also affects species. Here, we evaluate whether an integrative metric of land-use intensity, the human appropriation of net primary production, is correlated with the decline of SR in used landscapes across the globe.

Location

Global.

Time period

Present.

Major taxa studied

Birds, mammals and amphibians.

Methods

Based on species range maps (spatial resolution: 20 km × 20 km) and an area-of-habitat approach, we calibrated a “species–energy model” by correlating the SR of three groups of vertebrates with net primary production and biogeographical covariables in “wilderness” areas (i.e., those where available energy is assumed to be still at pristine levels). We used this model to project the difference between pristine SR and the SR corresponding to the energy remaining in used landscapes (i.e., SR loss expected owing to human energy extraction outside wilderness areas). We validated the projected species loss by comparison with the realized and impending loss reconstructed from habitat conversion and documented by national Red Lists.

Results

Species–energy models largely explained landscape-scale variation of mapped SR in wilderness areas (adjusted R²-values: 0.79–0.93). Model-based projections of SR loss were lower, on average, than reconstructed and documented ones, but the spatial patterns were correlated significantly, with stronger correlation in mammals (Pearson's r = 0.68) than in amphibians (r = 0.60) and birds (r = 0.57).

Main conclusions

Our results suggest that the human appropriation of net primary production is a useful indicator of heterotrophic species loss in used landscapes, hence we recommend its inclusion in models based on species–area relationships to improve predictions of land-use-driven biodiversity loss.     

Does hunting affect the demography and genetic structure of the greywing francolin Francolinus africanus?

Hunted and unhunted populations of greywing francolin Francolinus africanus have been studied in the eastern Cape Province of South Africa in order to understand the effects of hunting on the demography and genetic structure of these populations. Greywing population density cycled annually for both hunted and unhunted populations. However, there was an apparent pulse of immigration of sub-dominant birds, and earlier reproduction, in the hunted populations immediately after the winter hunting season. Average levels of allozyme heterozygosity (H) for hunted and unhunted populations were both 0.076, and although the proportion of polymorphic loci per sample and the mean number of alleles per locus for each sample were lower for the hunted populations than for the unhunted populations, these differences were not significant. However, the hunted populations displayed higher levels of outbreeding (lower F _IS and F _IT values) than those for unhunted populations. Therefore, it is concluded that although greywing francolin populations contain relatively high levels of genetic heterogeneity, it is probably the increased levels of local immigration following hunting which reduces the effects of any reduction in genetic variation due to a decrease in local population size from hunting.     

The Human Mast Cell Chymase Gene (CMA1): Mapping to the Cathepsin G/Granzyme Gene Cluster and Lineage-Restricted Expression

Genes encoding T-cell-receptor α/δ chains, neutrophil cathepsin G, and lymphocyte CGL/granzymes are closely linked on chromosomal band 14q11.2. The current work identifies the human mast cell chymase gene (CMA1) as the fourth protease in this cluster and maps the gene to within 150 kb of the cathepsin G gene. The gene order is centromere-T cell receptor α/δ-CGL-1/granzyme B-CGL-2/granzyme H-cathepsin G-chymase. Chymase and cathepsin G genes are shown to be cotranscribed in the human mast cell line HMC-1 and in U-937 cells. Other cells transcribe cathepsin G or CGL/granzyme genes, but not chymase genes, suggesting a capacity for independent regulation. Comparison of the 5′ flank of the chymase gene with those of cathepsin G and CGL/granzymes reveals little overall homology. Only short regions of the 5′ flanks of the human and murine chymase genes sequenced to date are similar, suggesting that they are more distantly related than human and rodent CGL-1/granzyme B, the flanks of which are highly homologous. The expression patterns and clustering of genes provide possible clues to the presence of locus control regions that orchestrate lineage-restricted expression of leukocyte and mast cell proteases.     

A 4D HCC(CO)NNH experiment for the correlation of aliphatic side-chain and backbone resonances in 13C/15N-labelled proteins

Summary We recently proposed a novel four-dimensional (4D) NMR strategy for the assignment of backbone nuclei in spectra of ¹³C/¹⁵N-labelled proteins (Boucher et al. (1992) J. Am. Chem. Soc., 114, 2262–2264 and J. Biomol. NMR, 2, 631–637). In this paper we extend this approach with a new constant time 4D HCC(CO)NNH experiment that also correlates the chemical shifts of the aliphatic sidechain (¹H and ¹³C) and backbone (¹H, ¹³C and ¹⁵N) nuclei. It separates the sidechain resonances, which may heavily overlap in spectra of proteins with large numbers of similar residues, according to the backbone nitrogen and amide proton chemical shifts. When used in conjunction with a 4D HCANNH or HNCAHA experiment it allows, in principle, complete assignment of aliphatic sidechain and backbone resonances with just two 4D NMR experiments.     

Molecular mimicry in Lyme disease: monoclonal antibody H9724 to b. burgdorferi flagellin specifically detects chaperonin-HSP60

A monoclonal antibody (H9724), specific for the 41-kDa flagellar protein of the Lyme disease pathogen Borrelia burgdorferi, cross-reacts with human axons and detects one major protein in human neuroblastoma cell extracts. The homologous cross-reacting protein has now been isolated from calf adrenal and identified as chaperonin-HSP60 by N-terminal sequencing.     

Computer assisted signal co-localization for simultaneous detection of antigen by immunohistochemistry and DNA by non-isotopic in situ hybridization

A method has been developed to co-localize signals for antigen and DNA using a desktop microcomputer system (computer assisted signal co-localization). Antigens were detected by standard immunohistochemical methods and DNA was detected by non-isotopic in situ hybridization (NISH). Using this method, NISH signals can be precisely located in cells with well-preserved morphology captured by computer. The removal of the first immunohistochemical reaction products and reagents eliminates possible interference with hybridization and non-specific binding to the probe; therefore the sensitivity of the original NISH method remains. The captured NISH signals can be converted to any other colour which contrasts with the immunostaining. We have used detection of Epstein-Barr virus (EBV) and keratins as a model system. This method is straightforward, and with necessary modifications, will be applicable to any type of combined immunohistochemistry and in situ hybridization technique for simultaneous detection of antigen and nucleic acids or two types of nucleic acids in the same cells.     

A latent class model to multiply impute missing treatment indicators in observational studies when inferences of the treatment effect are made using propensity score matching

Analysts often estimate treatment effects in observational studies using propensity score matching techniques. When there are missing covariate values, analysts can multiply impute the missing data to create m completed data sets. Analysts can then estimate propensity scores on each of the completed data sets, and use these to estimate treatment effects. However, there has been relatively little attention on developing imputation models to deal with the additional problem of missing treatment indicators, perhaps due to the consequences of generating implausible imputations. However, simply ignoring the missing treatment values, akin to a complete case analysis, could also lead to problems when estimating treatment effects. We propose a latent class model to multiply impute missing treatment indicators. We illustrate its performance through simulations and with data taken from a study on determinants of children's cognitive development. This approach is seen to obtain treatment effect estimates closer to the true treatment effect than when employing conventional imputation procedures as well as compared to a complete case analysis.     

Odontocete spatial patterns and temporal drivers of detection at sites in the Hawaiian islands

Successful conservation and management of marine top predators rely on detailed documentation of spatiotemporal behavior. For cetacean species, this information is key to defining stocks, habitat use, and mitigating harmful interactions. Research focused on this goal is employing methodologies such as visual observations, tag data, and passive acoustic monitoring (PAM) data. However, many studies are temporally limited or focus on only one or few species. In this study, we make use of an existing long-term (2009–2019), labeled PAM data set to examine spatiotemporal patterning of at least 10 odontocete (toothed whale) species in the Hawaiian Islands using compositional analyses and modeling techniques. Species composition differs among considered sites, and this difference is robust to seasonal movement patterns. Temporally, hour of day was the most significant predictor of detection across species and sites, followed by season, though patterns differed among species. We describe long-term trends in species detection at one site and note that they are markedly similar for many species. These trends may be related to long-term, underlying oceanographic cycles that will be the focus of future study. We demonstrate the variability of temporal patterns even at relatively close sites, which may imply that wide-ranging models of species presence are missing key fine-scale movement patterns. Documented seasonal differences in detection also highlights the importance of considering season in survey design both regionally and elsewhere. We emphasize the utility of long-term, continuous monitoring in highlighting temporal patterns that may relate to underlying climatic states and help us predict responses to climate change. We conclude that long-term PAM records are a valuable resource for documenting spatiotemporal patterns and can contribute many insights into the lives of top predators, even in highly studied regions such as the Hawaiian Islands.     

A transformer architecture for retention time prediction in liquid chromatography mass spectrometry-based proteomics

Accurate retention time (RT) prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision, and biology. We assess the performance of the transformer architecture for RT prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.