Johnsenbaxter6882

Infection by pathogens is strongly affected by the diet or condition of the prospective host. Studies that examine the impact of diet have mainly focused on single pathogens; however, co-infections within a single host are thought to be common. Different pathogen groups might respond differently to resource availability and diverse infections could increase the costs of host defense, meaning the outcome of mixed infections under varying dietary regimes is likely to be hard to predict. We used the generalist cabbage looper, Trichoplusia ni and two of its pathogens, the DNA virus T. ni nucleopolyhedrovirus (TniSNPV) and the entomopathogenic fungus, Beauveria bassiana to examine how nutrient reduction affected the outcome of mixed pathogen infection. We challenged insects with a low or high effective dose of virus, alone or combined with a single dose of fungus. We manipulated food availability after pathogen challenge by diluting artificial diet with cellulose, a non-nutritious bulking agent, and examined its impact on host and pathogen fitness. Reducing diet quantity did not alter overall or pathogen-specific mortality. In all cases, TniSNPV-induced mortality was negatively affected by fungus challenge. Similarly, B. bassiana-induced mortality was negatively affected by TniSNPV challenge, but only at the higher virus dose. Dietary dilution mainly affected B. bassiana speed of kill when mixed with a high dose of TniSNPV, with an increase in the duration of fungal infection when cellulose was low (high quantity). One pathogen dominated the production of transmission stages in the cadavers and co-infection did not affect the yield of either pathogen. There was no evidence that co-infections were more costly to the survivors of pathogen challenge. In conclusion, dietary dilution did not determine the outcome of mixed pathogen infection, but it had more subtle effects, that differed between the two pathogens and could potentially alter pathogen recycling and host-pathogen dynamics.Understanding the risk of local extinction of a species is vital in conservation biology, especially now when anthropogenic disturbances and global warming are severely changing natural habitats. Local extinction risk depends on species traits, such as its geographical range size, fresh body mass, dispersal ability, length of flying period, life history variation, and how specialized it is regarding its breeding habitat. We used a phylogenetic approach because closely related species are not independent observations in the statistical tests. Our field data contained the local extinction risk of 31 odonate (dragonflies and damselflies) species from Central Finland. Species relatedness (i.e., phylogenetic signal) did not affect local extinction risk, length of flying period, nor the geographical range size of a species. However, we found that closely related species were similar in hind wing length, length of larval period, and habitat of larvae. Both phylogenetically corrected (PGLS) and uncorrected (GLM) analysis indicated that the geographical range size of species was negatively related to local extinction risk. Contrary to expectations, habitat specialist species did not have higher local extinction rates than habitat generalist species nor was it affected by the relatedness of species. As predicted, species' long larval period increased, and long wings decreased the local extinction risk when evolutionary relatedness was controlled. Our results suggest that a relatively narrow geographical range size is an accurate estimate for a local extinction risk of an odonate species, but the species with long life history and large habitat niche width of adults increased local extinction risk. Because the results were so similar between PGLS and GLM methods, it seems that using a phylogenetic approach does not improve predicting local extinctions.Due to rapid urbanization, logging, and agricultural expansion, forest fragmentation is negatively affecting native wildlife populations throughout the tropics. This study examined the effects of landscape and habitat characteristics on the lesser mouse-deer, Tragulus kanchil, populations in Peninsular Malaysia. We conducted camera-trap survey at 315 sampling points located within 8 forest reserves. An assessment of site-level and landscape variables was conducted at each sampling point. Our study provides critical ecological information for managing and conserving understudied populations of T. kanchil. We found that the detection of T. kanchil was attributed to forest fragmentation in which forest patches had four times greater detection of T. kanchil than continuous forest. The detection of T. kanchil was nearly three times higher in peat swamp forest compared to lowland dipterocarp forests. Surprisingly, the detection of T. kanchil was higher in logged forests (logging ceased at least 30 years ago) than unlogged forests. The detection of T. kanchil increased with the presence of trees, particularly those with DBH of 5 cm to 45 cm, canopy cover, number of saplings and palms, number of dead fallen trees, and distance from nearest roads. However, detection decreased with a greater number of trees with DBH greater than 45 cm and higher elevations, and greater detections where creeping bamboo was abundant. We recommend that conservation stakeholders take the necessary steps (e.g., eradicating poaching, habitat degradation, and further deforestation) to support the conservation of mouse-deer species and its natural habitats.Fire regimes shape plant communities but are shifting with changing climate. More frequent fires of increasing intensity are burning across a broader range of seasons. Despite this, impacts that changes in fire season have on plant populations, or how they interact with other fire regime elements, are still relatively understudied. We asked (a) how does the season of fire affect plant vigor, including vegetative growth and flowering after a fire event, and (b) do different functional resprouting groups respond differently to the effects of season of fire? We sampled a total of 887 plants across 36 sites using a space-for-time design to assess resprouting vigor and reproductive output for five plant species. Sites represented either a spring or autumn burn, aged one to three years old. Season of fire had the clearest impacts on flowering in Lambertia formosa with a 152% increase in the number of plants flowering and a 45% increase in number of flowers per plant after autumn compared with spring fires. There were also season × severity interactions for total flowers produced for Leptospermum polygalifolium and L. trinervium with both species producing greater flowering in autumn, but only after lower severity fires. Severity of fire was a more important driver in vegetative growth than fire season. Season of fire impacts have previously been seen as synonymous with the effects of fire severity; however, we found that fire season and severity can have clear and independent, as well as interacting, impacts on post-fire vegetative growth and reproductive response of resprouting species. Overall, we observed that there were positive effects of autumn fires on reproductive traits, while vegetative growth was positively related to fire severity and pre-fire plant size.Simple sequence repeats (SSRs) are widely used genetic markers in ecology, evolution, and conservation even in the genomics era, while a general limitation to their application is the difficulty of developing polymorphic SSR markers. Next-generation sequencing (NGS) offers the opportunity for the rapid development of SSRs; however, previous studies developing SSRs using genomic data from only one individual need redundant experiments to test the polymorphisms of SSRs. In this study, we designed a pipeline for the rapid development of polymorphic SSR markers from multi-sample genomic data. We used bioinformatic software to genotype multiple individuals using resequencing data, detected highly polymorphic SSRs prior to experimental validation, significantly improved the efficiency and reduced the experimental effort. The pipeline was successfully applied to a globally threatened species, the brown eared-pheasant (Crossoptilon mantchuricum), which showed very low genomic diversity. The 20 newly developed SSR markers were highly polymorphic, the average number of alleles was much higher than the genomic average. We also evaluated the effect of the number of individuals and sequencing depth on the SSR mining results, and we found that 10 individuals and ~10X sequencing data were enough to obtain a sufficient number of polymorphic SSRs, even for species with low genetic diversity. Furthermore, the genome assembly of NGS data from the optimal number of individuals and sequencing depth can be used as an alternative reference genome if a high-quality genome is not available. Our pipeline provided a paradigm for the application of NGS technology to mining and developing molecular markers for ecological and evolutionary studies.Active learning in STEM education is essential for engaging the diverse pool of scholars needed to address pressing environmental and social challenges. However, active learning formats are difficult to scale and their incorporation into STEM teaching at U.S. universities varies widely. Here, we argue that urban agriculture as a theme can significantly increase active learning in undergraduate biology education by facilitating outdoor fieldwork and community-engaged education. check details We begin by reviewing benefits of field courses and community engagement activities for undergraduate biology and discuss constraints to their broader implementation. We then describe how urban agriculture can connect biology concepts to pressing global changes, provide field research opportunities, and connect students to communities. Next, we assess the extent to which urban agriculture and related themes have already been incorporated into biology-related programs in the United States using a review of major programs, reports on how ity contributions within required coursework, and help instructors feel a greater sense of accomplishment in an era of uncertainty.The foraging and nesting performance of bees can provide important information on bee health and is of interest for risk and impact assessment of environmental stressors. While radiofrequency identification (RFID) technology is an efficient tool increasingly used for the collection of behavioral data in social bee species such as honeybees, behavioral studies on solitary bees still largely depend on direct observations, which is very time-consuming. Here, we present a novel automated methodological approach of individually and simultaneously tracking and analyzing foraging and nesting behavior of numerous cavity-nesting solitary bees. The approach consists of monitoring nesting units by video recording and automated analysis of videos by machine learning-based software. This Bee Tracker software consists of four trained deep learning networks to detect bees that enter or leave their nest and to recognize individual IDs on the bees' thorax and the IDs of their nests according to their positions in the nesting unit. The software is able to identify each nest of each individual nesting bee, which permits to measure individual-based measures of reproductive success. Moreover, the software quantifies the number of cavities a female enters until it finds its nest as a proxy of nest recognition, and it provides information on the number and duration of foraging trips. By training the software on 8 videos recording 24 nesting females per video, the software achieved a precision of 96% correct measurements of these parameters. The software could be adapted to various experimental setups by training it according to a set of videos. The presented method allows to efficiently collect large amounts of data on cavity-nesting solitary bee species and represents a promising new tool for the monitoring and assessment of behavior and reproductive success under laboratory, semi-field, and field conditions.