Measuring inflammatory markers is critical to evaluating both recent infection status and overall human and animal health; however, there are relatively few techniques that do not require specialized equipment or personnel for detecting inflammation among wildlife. Such techniques are useful in that they help determine individual and population-level inflammatory status without the infrastructure and reagents that many more-specific assays require. One such technique, known as the erythrocyte sedimentation rate (ESR), is a measure of how quickly erythrocytes (red blood cells) settle in serum, with a faster rate indicating a general, underlying inflammatory process is occurring. The technique is simple, inexpensive, and can be performed in the field without specialized equipment. We took advantage of a population of African buffalo (Syncerus caffer), well studied from June 2014 to May 2017, to understand the utility of ESR in an important wildlife species. When ESR was compared with other markers of immunity in African buffalo, it correlated to known measures of inflammation. We found that a faster ESR was significantly positively correlated with increased total globulin levels and significantly negatively correlated with increased red blood cell count and albumin levels. We then evaluated if ESR correlated to the incidence of five respiratory pathogens and infection with two tick-borne pathogens in African buffalo. Our results suggest that elevated ESR is associated with the incidence of bovine viral diarrhea virus infection, parainfluenza virus, and Mannheimia haemolytica infections as well as concurrent Anaplasma marginale and Anaplasma centrale coinfection. These findings suggest that ESR is a useful field test as an inflammatory marker in individuals and herds, helping us better monitor overall health status in wild populations.

Diatom assemblages are excellent indicators for environmental monitoring. However, enumerating diatoms using fine-level taxonomy takes considerable effort, which must be undertaken by specialist taxonomists. One alternative is to enumerate assemblages using morphological traits. In this study, we compared the accuracy of models using 20 morphological traits with those using species assemblages to infer lake water pH, salinity, depth, and total phosphorus concentrations in four data sets, each comprising over 200 lakes. Assemblages aggregated by trait combinations were used to predict environmental variables via weighted averaging regressions, and richness of trait combinations was regressed against the environmental variables. Trait-based weighted averaging regressions showed slightly lower accuracy than species-level analyses and higher accuracy than analyses at the family and sometimes genus level. Richness of trait combinations showed relationships with pH, salinity, and lake depth that were marginally stronger than relationships using species richness. Although species-level analyses are the best approach when time and budgets allow, we suggest that trait combinations could provide an alternative method for water quality assessment programs, where funds do not allow the use of specialist taxonomists or where diatoms are being used as part of a multi-indicator analysis.

Newborn mammals have an immature immune system that cannot sufficiently protect them against infectious diseases. However, variation in the effectiveness of maternal immunity against different parasites may couple with temporal trends in parasite exposure to influence disparities in the timing of infection risk. Determining the relationship between age and infection risk is critical in identifying the portion of a host population that contributes to parasite dynamics, as well as the parasites that regulate host recruitment. However, there are no data directly identifying timing of first infection among parasites in wildlife. Here, we took advantage of a longitudinal dataset, tracking infection status by viruses, bacteria, protists and gastro-intestinal worms in a herd of African buffalo ( Syncerus caffer ) to ask: how does age of first infection differ among parasite taxa? We found distinct differences in the age of first infection among parasites that aligned with the mode of transmission and parasite taxonomy. Specifically, we found that tick-borne and environmentally transmitted protists were acquired earlier than directly transmitted bacteria and viruses. These results emphasize the importance of understanding infection risk in juveniles, especially in host species where juveniles are purported to sustain parasite persistence and/or where mortality rates of juveniles influence population dynamics.

Bovine anaplasmosis is a tick-borne disease, mainly caused by Anaplasma marginale and A. centrale and is distributed in tropical and sub-tropical areas. This study aimed to characterise A. marginale and A. centrale from African buffaloes in Kruger National Park (KNP), South Africa, using the DNA sequences of the genes coding for major surface protein (msp1β) and heat shock protein (groEL), respectively. A total of 747 blood samples were collected from February 2014 to August 2016 from African buffaloes kept in KNP, and DNAs were tested using a molecular-phylogenetic approach. Out of 747 samples tested, 129 (17.3%) and 98 (13.1%) were positive for single infection with A. marginale and A. centrale, respectively; whereas 113 (15.1%) were positive for both Anaplasma spp. Pairwise difference of 1.6–8.5% was observed in msp1β sequences of A. marginale whereas that was only 0.3–2.4% for groEL sequences of A. centrale. Separate phylogenetic analyses of msp1β and groEL sequences of A. marginale and A. centrale, respectively, revealed that sequences of Anaplasma spp. from African buffaloes were unique and they grouped separately when compared with previously published sequences of both species. This is the first study to characterise A. marginale and A. centrale from African buffalo using species specific molecular markers. This study will pave the way for future studies to assess genetic variation among Anaplasma spp. from wild ruminants using molecular markers that are better at differentiating between species and strains than the more commonly used 16S rRNA gene, and help to undertake health and fitness studies and host-parasite dynamics using quantitative molecular tools.

Summary Predictive models relating ecological assemblages to environmental conditions are widely used in environmental impact assessment and biomonitoring. Such models are often parameterized using comprehensive ecological sampling and taxonomic identification efforts. Limited resources mean that expensive sampling and analytical procedures should be planned to maximize information gain and minimize unnecessary expense. However, there has been little consideration of cost‐effectiveness in parameterizing predictive models using ecological assemblages and no explicit consideration of cost‐effectiveness in balancing investment in the crucial aspects of sample size and taxonomic resolution. Using lacustrine diatom (Bacillariophyceae) assemblages from four large‐scale (c. 77 000–1·3 million km2) data sets containing between 207 and 493 lakes, we address the following questions: (1) how does taxonomic resolution affect information content; (2) how does sample size affect information content for different taxonomic resolutions; and (3) what are the most cost‐effective strategies for constructing environmental assessment models using diatom assemblages across a range of budgets? We use weighted averaging regression models for pH, phosphorus, salinity and lake depth and realistic data collection costs to examine the relationship between cost and model information content (R2 and root mean squared error of prediction). For diatom‐based models, finer taxonomic resolutions almost always provide more cost‐effective information content than collecting more samples, with (morpho)species being the most appropriate taxonomic resolution for nearly all budget scenarios. Information content exhibits an asymptotic relationship with sample size and budget, with greatest information gain during initial sample size increases, and little gain beyond c. 100 samples. Smaller sample sizes can also achieve surprising predictive power in some cases, suggesting low‐cost regional models may be achievable. However, caution is necessary in such an approach, because spatial dependencies in predictions may be missed and analogues with predicted assemblages may be poor. Synthesis and applications. We demonstrate the utility of explicitly considering cost estimates to determine optimal sampling effort and taxonomic resolution for ecological assemblage models. For large, regional biomonitoring programmes, cost‐effective sampling could save millions of dollars. Our framework for determining optimal trade‐offs in ecological assemblage models is easily adaptable to other taxa and analytical techniques used in biomonitoring and environmental assessment.