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Francesca T. Erickson

Publications and source records attributed to Francesca T. Erickson.

2 recordsLinked to original sources

Chew-cards can accurately index invasive rat densities in Mariana Island forests

Rats ( Rattus spp.) are likely established on 80–90% of the world’s islands and represent one of the most damaging and expensive biological invaders. Effective rat control tools exist but require accurate population density estimates or indices to inform treatment timing and effort and to assess treatment efficacy. Capture-mark-recapture data are frequently used to produce robust density estimates, but collecting these data can be expensive, time-consuming, and labor-intensive. We tested a potentially cheaper and easier alternative, chew-cards, as a count-based (quantitative) index of invasive rat densities in tropical forests in the Mariana Islands, an archipelago in the western North Pacific Ocean. We trialed chew-cards in nine forest grids on two Mariana Islands by comparing the proportion of cards chewed to capture-mark-recapture density estimates and manipulated rat densities to test whether the relationship was retained. Chew-card counts were positively correlated with rat capture-mark-recapture density estimates across a range of rat densities found in the region. Additionally, the correlation between the two sampling methods increased with the number of days chew-cards were deployed. Specifically, when chew-cards were deployed for five nights, a 10% increase in the proportion of cards chewed equated to an estimated increase in rat density of approximately 2.4 individuals per ha (R 2 = 0.74). Chew-cards can provide a valid index of rat densities in Mariana Island forests and are a cheaper alternative to capture-mark-recapture sampling when relative differences in density are of primary interest. New cost-effective monitoring tools can enhance our understanding and management of invaded islands while stretching limited resources further than some conventional approaches, thus improving invasive species management on islands.

NeoBiota

Decision context as an essential component of population viability analysis

Population viability analysis (PVA) is a widely used tool that applies demographic data in simulation frameworks to assess extinction risk for species or populations. It is used in diverse conservation applications, including evaluating management effectiveness, relative risk of threats, and potential changes to protective status (Beissinger & McCullough, 2002 ), and can be a critical tool for making decisions with imperfect knowledge of the system state, often on limited timelines (Meine et al., 2006 ). Chaudhary and Oli ( 2020 ) recently developed a framework to appraise the quality of PVAs based on the presence of essential background, model, and analysis components. They evaluated 160 published PVAs and reported a decline in the quality of PVAs over time (1990−2017). We agree PVA studies should report unambiguous descriptions of their essential components (Table 1 in Chaudhary and Oli) and explicitly state the model's biological and statistical assumptions. The need for increased transparency in PVAs is evident. Morrison et al. ( 2016 ) reported that only 50% of PVAs published in peer-reviewed and gray literature were both reproducible and repeatable. Further, in an examination of 67 studies that used matrix population models (widely used in PVAs), Kendall et al. ( 2019 ) reported that models frequently contained misspecification errors. Given the rapid advancement of simulation techniques, updated guidance for PVA construction is warranted. However, we believe the essential PVA components identified by Chaudhary and Oli contain a critical omission: the decision context in which the PVA was created and its usefulness in that context. Quality and utility are not mutually exclusive; however, some models that do not meet idealized quality standards might still be valuable because they are useful and represent the best available science for a given decision context (hereafter, decision-support models). The definition of quality for decision-support models should be different than models developed for the purpose of learning (hereafter, heuristic models) and should incorporate how useful the model was, despite information gaps. We further argue that assessment questions should be used prospectively to guide modeling projects, rather than for retrospective comparison of model quality.

Conservation Biology