Best environmental predictors of breeding phenology differ with elevation in a common woodland bird species

Temperatures in mountain areas are increasing at a higher rate than the Northern Hemisphere land average, but how fauna may respond, in particular in terms of phenology, remains poorly understood. The aim of this study was to assess how elevation could modify the relationships between climate variability (air temperature and snow melt‐out date), the timing of plant phenology and egg‐laying date of the coal tit (Periparus ater). We collected 9 years (2011–2019) of data on egg‐laying date, spring air temperature, snow melt‐out date, and larch budburst date at two elevations (~1,300 m and ~1,900 m asl) on a slope located in the Mont‐Blanc Massif in the French Alps. We found that at low elevation, larch budburst date had a direct influence on egg‐laying date, while at high‐altitude snow melt‐out date was the limiting factor. At both elevations, air temperature had a similar effect on egg‐laying date, but was a poorer predictor than larch budburst or snowmelt date. Our results shed light on proximate drivers of breeding phenology responses to interannual climate variability in mountain areas and suggest that factors directly influencing species phenology vary at different elevations. Predicting the future responses of species in a climate change context will require testing the transferability of models and accounting for nonstationary relationships between environmental predictors and the timing of phenological events.     

Anti-Sporothrix Antibody Detection in Domestic Cats as an Indicator of a Possible New Occurrence Area for Sporotrichosis in North Brazil

Mycopathologia - Feline sporotrichosis has emerged as an important public health issue in some countries, especially Brazil. Currently, zoonotic transmission of Sporothrix brasiliensis by domestic...     

Chemical synthesis and characterization of wild‐type and biotinylated N‐terminal domain 1–64 of β2‐glycoprotein I

The antiphospholipid syndrome (APS) is a severe autoimmune disease associated with recurrent thrombosis and fetal loss and characterized by the presence of circulating autoantibodies (aAbs) mainly recognizing the N‐terminal domain (DmI) of β2‐glycoprotein I (β2GpI). To possibly block anti‐β2GpI Abs activity, we synthesized the entire DmI comprising residues 1–64 of β2GpI by chemical methods. Oxidative disulfide renaturation of DmI was achieved in the presence of reduced and oxidized glutathione. The folded DmI (N‐DmI) was purified by RP‐HPLC, and its chemical identity and correct disulfide pairing (Cys4‐Cys47 and Cys32‐Cys60) were established by enzymatic peptide mass fingerprint analysis. The results of the conformational characterization, conducted by far‐ and near‐UV CD and fluorescence spectroscopy, provided strong evidence for the native‐like structure of DmI, which is also quite resistant to both Gdn‐HCl and thermal denaturation. However, the thermodynamic stability of N‐DmI at 37°C was remarkably low, in agreement with the unfolding energetics of small proteins. Of note, aAbs failed to bind to plates coated with N‐DmI in direct binding experiments. From ELISA competition experiments with plate‐immobilized β2GpI, a mean IC₅₀ value of 8.8 μM could be estimated for N‐DmI, similar to that of the full‐length protein, IC₅₀(β2GpI) = 6.4 μM, whereas the cysteine‐reduced and carboxamidomethylated DmI, RC‐DmI, failed to bind to anti‐β2GpI Abs. The versatility of chemical synthesis was also exploited to produce an N‐terminally biotin‐(PEG)₂‐derivative of N‐DmI (Biotin‐N‐DmI) to be possibly used as a new tool in APS diagnosis. Strikingly, Biotin‐N‐DmI loaded onto a streptavidin‐coated plate selectively recognized aAbs from APS patients.     

Combining point‐process and landscape vegetation models to predict large herbivore distributions in space and time—A case study of Rupicapra rupicapra

Aim

When modelling the distribution of animals under current and future conditions, both their response to environmental constraints and their resources’ response to these environmental constraints need to be taken into account. Here, we develop a framework to predict the distribution of large herbivores under global change, while accounting for changes in their main resources. We applied it to Rupicapra rupicapra, the chamois of the European Alps.

Location

The Bauges Regional Park (French Alps).

Methods

We built sixteen plant functional groups (PFGs) that account for the chamois’ diet (estimated from sequenced environmental DNA found in the faeces), climatic requirements, dispersal limitations, successional stage and interaction for light. These PFGs were then simulated using a dynamic vegetation model, under current and future climatic conditions up to 2100. Finally, we modelled the spatial distribution of the chamois under both current and future conditions using a point‐process model applied to either climate‐only variables or climate and simulated vegetation structure variables.

Results

Both the climate‐only and the climate and vegetation models successfully predicted the current distribution of the chamois species. However, when applied into the future, the predictions differed widely. While the climate‐only models predicted an 80% decrease in total species occupancy, including vegetation structure and plant resources for chamois in the model provided more optimistic predictions because they account for the transient dynamics of the vegetation (?20% in species occupancy).

Main conclusions

Applying our framework to the chamois shows that the inclusion of ecological mechanisms (i.e., plant resources) produces more realistic predictions under current conditions and should prove useful for anticipating future impacts. We have shown that discounting the pure effects of vegetation on chamois might lead to overpessimistic predictions under climate change. Our approach paves the way for improved synergies between different fields to produce biodiversity scenarios.