The rapid growth and increasing popularity of smartphone technology is putting sophisticated data-collection tools in the hands of more and more citizens. This has exciting implications for the expanding field of citizen science. With smartphone-based applications (apps), it is now increasingly practical to remotely acquire high quality citizen-submitted data at a fraction of the cost of a traditional study. Yet, one impediment to citizen science projects is the question of how to train participants. The traditional “in-person” training model, while effective, can be cost prohibitive as the spatial scale of a project increases. To explore possible solutions, we analyze three training models: 1) in-person, 2) app-based video, and 3) app-based text/images in the context of invasive plant identification in Massachusetts. Encouragingly, we find that participants who received video training were as successful at invasive plant identification as those trained in-person, while those receiving just text/images were less successful. This finding has implications for a variety of citizen science projects that need alternative methods to effectively train participants when in-person training is impractical.

The challenge of maintaining sufficient food, feed, fiber, and forests, for a projected end of century population of between 9-10 billion in the context of a climate averaging 2-4 • C warmer, is a global imperative. However, climate change is likely to alter the geographic ranges and impacts for a variety of insect pests, plant pathogens, and weeds, and the consequences for managed systems, particularly agriculture, remain uncertain. That uncertainty is related, in part, to whether pest management practices (e.g., biological, chemical, cultural, etc.) can adapt to climate/CO 2 induced changes in pest biology to minimize potential loss. The ongoing and projected changes in CO 2 , environment, managed plant systems, and pest interactions, necessitates an assessment of current management practices and, if warranted, development of viable alternative strategies to counter damage from invasive alien species and evolving native pest populations. We provide an overview of the interactions regarding pest biology and climate/CO 2 ; assess these interactions currently using coffee as a case study; identify the potential vulnerabilities regarding future pest impacts; and discuss possible adaptive strategies, including early detection and rapid response via EDDMapS (Early Detection & Distribution Mapping System), and integrated pest management (IPM), as adaptive means to improve monitoring pest movements and minimizing biotic losses while improving the efficacy of pest control.can encompass simple to sophisticated strategies (e.g., from hoeing to modifying the environment to utilize ecosystem services) to manage pest populations and ensuing damage.Recent and projected increases in atmospheric carbon dioxide (CO 2 ) concentration are expected to continue with a potential 2× increase over current CO 2 levels, and subsequent, concomitant increases in average temperature between 0.15 and 0.3 • C, per decade, by 2100 [3,4]. Such projections, as well as recent potential changes in extreme events, increase the degree of uncertainty of how these environmental changes could impact pest biology (insects, plant pathogens, weeds), and the consequences for future biotic losses from managed plant systems.Recent and projected increases in atmospheric CO 2 could change pest biology in two essential ways. The first is related to physical changes in the environment incurred as CO 2 increases. Such increases, along with other radiation trapping gases (e.g., CH 4 , N 2 O), will increase surface temperatures [5], cause changes in precipitation frequency [6], and alter the diurnal temperature range (DTR) [7], as well as the magnitude and distribution of extreme weather events [8]. A second essential consequence is the "fertilization" effect of rising CO 2 on plant photosynthesis; approximately 95% of plant species, those that rely solely on the C 3 photosynthetic pathway, could increase growth and reproduction as CO 2 increases, including agronomic and invasive weeds. There are hundreds of studies and several meta-analyses showing that both recen...

Spatial abundance information is a critical component of invasive plant risk assessment. While spatial occurrence data provide important information about potential establishment, abundance data are necessary to understand invasive species’ populations, which ultimately drive environmental and economic impacts. In recent years, the collective efforts of numerous management agencies and public participants have created unprecedented spatial archives of invasive plant occurrence, but consistent information about abundance remains rare. Here, we develop guidelines for the collection and reporting of abundance information that can add value to existing data collection efforts and inform spatial ecology research. In order to identify the most common methods used to report abundance, we analyzed over 1.6 million invasive plant records in the Early Detection and Distribution Mapping System (EDDMapS). Abundance data in some form are widely reported, with 58.9% of records containing qualitative or quantitative information about invasive plant cover, density, or infested area, but records vary markedly in terms of standards for reporting. Percent cover was the most commonly reported metric of abundance, typically collected in bins of trace (<1%), low (1–5%), moderate (5–25%), and high (>25%). However, percent cover data were rarely reported along with an estimate of area, which is critical for ensuring accurate interpretation of reported abundance data. Infested area is typically reported as a number with associated units of square feet or acres. Together, an estimate of both cover and infested area provides the most robust and interpretable information for spatial research and risk assessment applications. By developing consistent metrics of reporting for abundance, collectors can provide much needed information to support spatial models of invasion risk.

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