Feeding sorghum ergot (Claviceps africana) to sows before farrowing inhibits milk production

Objective: To assess the impact of feeding different amounts of sorghum ergot to sows before farrowing. Design: Fifty-one pregnant sows from a continually farrowing piggery were sequentially inducted into the experiment each week in groups of four to seven, as they approached within 14 days of farrowing. Diets containing sorghum ergot sclerotia within the range of 0 (control) up to 1.5% w/w (1.5% ergot provided 7 mg alkaloids/kg, including 6 mg dihydroergosine/kg) were randomly allocated and individually fed to sows. Ergot concentrations were varied with each subsequent group until an acceptable level of tolerance was achieved. Diets with ergot were replaced with control diets after farrowing. Post-farrowing milk production was assessed by direct palpation and observation of udders, and by piglet responses and growth. Blood samples were taken from sows on three days each week, for prolactin estimation. Results: Three sows fed 1.5% ergot for 6 to 10 days preceding farrowing produced no milk, and 87% of their piglets died despite supplementary feeding of natural and artificial colostrums, milk replacer, and attempts to foster them onto normally lactating sows. Ergot inclusions of 0.6% to 1.2% caused lesser problems in milk release and neo-natal piglet mortality. Of 23 sows fed either 0.3% or 0.6% ergot, lactation of only two first-litter sows were affected. Ergot caused pronounced reductions in blood prolactin, and first-litter sows had lower plasma prolactin than multiparous sows, increasing their susceptibility to ergot. Conclusion: Sorghum ergot should not exceed 0.3% (1 mg alkaloid/kg) in diets of multiparous sows fed before farrowing, and should be limited to 0.1 % for primiparous sows, or avoided completely.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.