Levon Biss 
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Climate change is predicted to disrupt patterns of local adaptation in wild and cultivated maize 
Aguirre-Liguori et al., 2019

Climate change is one of the most important threats to biodiversity and crop sustainability. The impact of climate change is often evaluated on the basis of expected changes in species' geographical distributions. Genomic diversity, local adaptation, and migration are seldom integrated into future species projections. Here, we examine how climate change will impact populations of two wild relatives of maize, the teosintes Zea mays ssp. mexicana and Z. mays ssp. parviglumis. Despite high levels of genetic diversity within populations and widespread future habitat suitability, we predict that climate change will alter patterns of local adaptation and decrease migration probabilities in more than two-thirds of present-day teosinte populations. These alterations are geographically heterogeneous and suggest that the possible impacts of climate change will vary considerably among populations. The population-specific effects of climate change are also evident in maize landraces, suggesting that climate change may result in maize landraces becoming maladapted to the climates in which they are currently cultivated. The predicted alterations to habitat distribution, migration potential, and patterns of local adaptation in wild and cultivated maize raise a red flag for the future of populations. The heterogeneous nature of predicted populations’ responses underscores that the selective impact of climate change may vary among populations and that this is affected by different processes, including past adaptation.

https://bit.ly/2JIB6BO

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The Birds

The world begins again!
Not wholly insufflated
the blackbirds in the rain
upon the dead topbranches
of the living tree,
stuck fast to the low clouds,
notate the dawn.
Their shrill cries sound
announcing appetite
and drop among the bending roses
and the dripping grass.
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William Carlos Williams
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Niños de todo el mundo  rodeados de lo que comen en una semana
Fotos: Gregg Segal 

https://bit.ly/2NIgY8n
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Many unreported crop pests and pathogens are probably already present 
Bebber et al., 2019.

Invasive species threaten global biodiversity, food security and ecosystem function. Such incursions present challenges to agriculture where invasive species cause significant crop damage and require major economic investment to control production losses. Pest risk analysis (PRA) is key to prioritize agricultural biosecurity efforts, but is hampered by incomplete knowledge of current crop pest and pathogen distributions. Here, we develop predictive models of current pest distributions and test these models using new observations at subnational resolution. We apply generalized linear models (GLM) to estimate presence probabilities for 1,739 crop pests in the CABI pest distribution database. We test model predictions for 100 unobserved pest occurrences in the People's Republic of China (PRC), against observations of these pests abstracted from the Chinese literature. This resource has hitherto been omitted from databases on global pest distributions. Finally, we predict occurrences of all unobserved pests globally. Presence probability increases with host presence, presence in neighbouring regions, per capita GDP and global prevalence. Presence probability decreases with mean distance from coast and known host number per pest. The models are good predictors of pest presence in provinces of the PRC, with area under the ROC curve (AUC) values of 0.75–0.76. Large numbers of currently unobserved, but probably present pests (defined here as unreported pests with a predicted presence probability >0.75), are predicted in China, India, southern Brazil and some countries of the former USSR. We show that GLMs can predict presences of pseudoabsent pests at subnational resolution. The Chinese literature has been largely inaccessible to Western academia but contains important information that can support PRA. Prior studies have often assumed that unreported pests in a global distribution database represent a true absence. Our analysis provides a method for quantifying pseudoabsences to enable improved PRA and species distribution modelling.

Total number of probably present pests in all countries and subnational regions

https://bit.ly/2XHgkfF

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The Peach Orchard
Akira Kurosawa's Dreams

Gert van den Bosch
@gertvanden

Maize stem
https://cytomine.coop/collection/maize-stem-slide
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Bengal monitor lizard by the artist Haludar, c.1805

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What we have been accustomed to calling 'the environment' might better be envisaged as a domain of entanglement

Tim Ingold 
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Synchronous crop failures and climate-forced production
Anderson et al., 2019.

Large-scale modes of climate variability can force widespread crop yield anomalies and are therefore often presented as a risk to food security. We quantify how modes of climate variability contribute to crop production variance. We find that the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), tropical Atlantic variability (TAV), and the North Atlantic Oscillation (NAO) together account for 18, 7, and 6% of globally aggregated maize, soybean, and wheat production variability, respectively. The lower fractions of global-scale soybean and wheat production variability result from substantial but offsetting climate-forced production anomalies. All climate modes are important in at least one region studied. In 1983, ENSO, the only mode capable of forcing globally synchronous crop failures, was responsible for the largest synchronous crop failure in the modern historical record. Our results provide the basis for monitoring, and potentially predicting, simultaneous crop failures.

 

Harvested area of wheat, maize, and soybean with numbered boxes indicating regions for the variance analysis (A). Percent of national or subnational scale variance in each region for wheat (B), soybean (C), and maize (D) explained by the ENSO (El Nino Southern Oscillation), IOD, TAV, or NAO. The percent values on top of each bar indicate the total variance explained by modes of climate variability (ENSO + TAV + IOD + NAO).

https://bit.ly/2JnxG8C

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The Dodel-Port Atlas (1878-1883)
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