We address criticism that the Transport, Establishment, Abundance, Spread, Impact (TEASI) framework does not facilitate objective mapping of risk assessment methods nor defines best practice. We explain why TEASI is appropriate for mapping, despite inherent challenges, and how TEASI offers considerations for best practices, rather than suggesting one best practice. Our review of alien species risk assessments (RA) (Leung et al. 2012) aimed to synthesise the diverse approaches applied in this field to establish a logical framework for best practices. We believe the TEASI framework that makes explicit the consideration of Transport, Establishment, Abundance, Spread and Impact aspects of biological invasions helps integrate the main ideas underlying risk assessment and identifies important open questions. Barry (2013) provided a thoughtful review of our study and while he found much to commend in our approach, he indicated two main criticisms: (1) the mapping process in the article was subjective and TEASI does not encapsulate all the reviewed RAs and (2) we are not explicit in defining the best practice. We address each criticism. First, although quantitative approaches were relatively easy to map onto the TEASI framework, scoring-based approaches were more difficult and more subjective. Importantly, subjective does not mean arbitrary. For instance, mapping RA questions such as 'propa-gules dispersed by wind' onto the Spread component in TEASI and identifying it as a species trait is arguably logical. However, the rationale for how answers were combined was less clear for scoring approaches. For instance, many simply summed binary yes/no answers across all components, so we agree that they 'would need to be radically redefined' to map onto TEASI as many do not consider model structure. Barry (2013) further notes that the scoring approaches 'are abstract while the TEASI model is process-based and explicit'. This is certainly true but if the 'abstract' risks do not (at least imperfectly) map onto the set of real processes underlying invasions, we question whether they can be predictive. Thus, we argue that scoring-based approaches can and should be considered in the context of a process-based framework, but we acknowledge that this is challenging. We view this difficulty in mapping model structure as a limitation of existing scoring methodology rather than of the process-based TEASI model. We pose the questions: do the scoring model structures make sense in terms of invasion processes? How? If they do not, in the future, should they? Note, we do not deny the value of scoring RAs; they will remain important in addressing biological invasions, given limited time, data and resources. In addition, Barry (2013) argues that TEASI equations were too highly structured and prescriptive. Although we could have just listed factors thought to be relevant for invasion risk, this would be less valuable. Models are useful, in part, exactly because they are highly structured, presenting a clear picture of how we believe factors rel...

One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial agent (“animat”) evolves over thousands of generations to solve a navigation task in a simple, simulated environment. We compare the ability of these measures to predict high fitness with more conventional information-theoretic processing measures. As the animat adapts by increasing its “fit” to the world, information integration and processing increase commensurately along the evolutionary line of descent. We suggest that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory. A correlation of measures of information integration (but also information processing) and fitness strongly suggests that these measures reflect the functional complexity of the animat, and that such measures can be used to quantify functional complexity even in the absence of fitness data.

In 2006, a deadly Escherichia coli O157:H7 outbreak in bagged spinach was traced to California's Central Coast region, where >70% of the salad vegetables sold in the United States are produced. Although no definitive cause for the outbreak could be determined, wildlife was implicated as a disease vector. Growers were subsequently pressured to minimize the intrusion of wildlife onto their farm fields by removing surrounding noncrop vegetation. How vegetation removal actually affects foodborne pathogens remains unknown, however. We combined a fine-scale land use map with three datasets comprising ∼250,000 enterohemorrhagic E. coli (EHEC), generic E. coli, and Salmonella tests in produce, irrigation water, and rodents to quantify whether seminatural vegetation surrounding farmland is associated with foodborne pathogen prevalence in California's Central Coast region. We found that EHEC in fresh produce increased by more than an order of magnitude from 2007 to 2013, despite extensive vegetation clearing at farm field margins. Furthermore, although EHEC prevalence in produce was highest on farms near areas suitable for livestock grazing, we found no evidence of increased EHEC, generic E. coli, or Salmonella near nongrazed, seminatural areas. Rather, pathogen prevalence increased the most on farms where noncrop vegetation was removed, calling into question reforms that promote vegetation removal to improve food safety. These results suggest a path forward for comanaging fresh produce farms for food safety and environmental quality, as federal food safety reforms spread across ∼4.5 M acres of US farmland.agriculture | biodiversity | disease ecology | E. coli | foodborne pathogens