ABSTRACT. Wild populations face threats both from deterministic factors, e.g., habitat loss, overexploitation, pollution, and introduced species, and from stochastic events of a demographic, genetic, and environmental nature, including catastrophes. Inbreeding reduces reproductive fitness in naturally outbreeding species, but its role in extinctions of wild populations is controversial. To evaluate critically the role of inbreeding in extinction, we conducted realistic population viability analyses of 20 threatened species, with and without inbreeding depression, using initial population sizes of 50, 250, and 1000. Inbreeding markedly decreased median times to extinction by 28.5, 30.5, and 25% for initial populations of 50, 250, and 1000, respectively, and the impacts were similar across major taxa. The major variable explaining differences among species was initial population growth rate, whereas the impact of inbreeding was least in species with negative growth rates. These results demonstrate that the prospects for survival of threatened species will usually be seriously overestimated if genetic factors are disregarded, and that inappropriate recovery plans may be instituted if inbreeding depression is ignored.

Digital data are accumulating at unprecedented rates. These contain a lot of information about the natural world, some of which can be used to answer key ecological questions. Here, we introduce iEcology (i.e., internet ecology), an emerging research approach that uses diverse online data sources and methods to generate insights about species distribution over space and time, interactions and dynamics of organisms and their environment, and anthropogenic impacts. We review iEcology data sources and methods, and provide examples of potential research applications. We also outline approaches to reduce potential biases and improve reliability and applicability. As technologies and expertise improve, and costs diminish, iEcology will become an increasingly important means to gain novel insights into the natural world. Information Age, Big Data, and iEcologyThe information age is characterized by rapid accumulation of myriad types of digital data [1]. Central to this revolution is the Internet, which is a source of unprecedented amounts of diverse and readily accessible data, via webpages, social media, and various other data platforms. These data are constantly created and stored in the digital realm and form an omnipresent part of the modern world. They also provide novel opportunities for research that the scientific community is only beginning to explore. Here, we describe an emerging research approach -iEcology (i.e., internet ecology), which we define as the study of ecological patterns and processes using online data generated for other purposes and stored digitally (Figure 1). These data can be used to address fundamental ecological questions and to analyze ecological processes at a range of spatiotemporal scales and across a diverse range of contexts. As such, iEcology has the potential to provide new understandings of ecological dynamics and mechanisms, complementing more traditional methods of obtaining ecological data.While iEcology can be considered to fit within the wider scope of ecological informatics (see Glossary), it is distinct from other uses of Big Data sources in the biological sciences in that data are not specifically and intentionally generated to address ecological and environmental questions [2][3][4]. Moreover, iEcology expands on the traditional scope of ecological informatics with new data sources and dedicated methods to analyze them. iEcology is predominantly focused on collecting, collating, and exploring data generated online by human society, either passively or unintentionally (e.g., Internet search activity, social media interactions, and uploaded data and media), a process also referred to as passive crowdsourcing [5]. iEcology uses digital methods to access, handle, and analyze these data, in a manner akin to techniques from other research fields such as sociology, culture and media studies, biomedical sciences, computer sciences, and economics [6,7]. iEcology also shares part of its toolbox with conservation culturomicsan emerging research area in conservation science [8-10]alb...

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The pattern of sightings of a species that is rare, and then no longer observed, can be used to estimate its extinction date. However, other than physical captures or specimens, the veracity of any sighting is ambiguous, and should be treated probabilistically when used to infer extinction dates. We present a simple yet powerful computational approach for incorporating observational reliability into extinction date estimators (EDE). Our method (1) combines repeated within‐year sightings probabilistically, (2) samples observations using reliability as an inclusion probability, (3) infers a probability distribution and summary statistics of extinction dates with any EDE, and (4) computes the frequency distribution of the extinction date. We applied this method to eight exemplar sighting records covering a range of lengths, sighting rates and uncertainties, using a variety of statistical EDEs, and compared these results with a threshold approach for selecting sightings. We also demonstrated a robust coverage of “true” extinction dates based on selected real‐world examples of rediscovered species and confirmed extinctions, and simulated sighting records. Our approach represents a powerful generalization of past work because it is not predicated on any specific method for inferring extinction dates, and yet is simple to implement (with R script provided).