Quantifying site-level usage and certainty of absence for an invasive species through occupancy analysis of camera-trap data

Designing a surveillance program for early detection of alien plants and insects in Norway

Naturalized species of alien plants and animals comprise < 3% of biodiversity recorded in Norway but have had major impacts on natural ecosystems through displacement of native species. Encroachment of alien species has been especially problematic for coastal sites close to transport facilities and urban areas with high density housing. The goal of our field project was to design and test a surveillance program for early detection of alien species of vascular plants and terrestrial insects at the first phase of establishment in natural areas. In our 3-year project (2018–2020), we sampled 60 study plots in three counties in the Oslofjord region of southern Norway. Study plots (6.25 ha) were selected by two criteria: manual selection based on expert opinion (27 plots) or by random selection based on weights from a hotspot analysis of occurrence of alien species (33 plots). Vascular plants were surveyed by two experienced botanists who found a total of 239 alien species of vascular plants in 95 rounds of surveys. Insects and other invertebrates were captured with a single Malaise trap per site, with 3–4 rounds of repeated sampling. We used DNA-metabarcoding to identify invertebrates based on DNA extractions from crushed insects or from the preservative media. Over 3500 invertebrate taxa were detected in 255 rounds of sampling. We recorded 20 alien species of known risk, and 115 species that were new to Norway, including several ‘doorknocker’ species identified by previous risk assessments. We modeled the probabilities of occupancy (ψ) and detection (p) with occupancy models with repeated visits by multiple observers (vascular plants) or multiple rounds of sampling (insects). The two probabilities covaried with risk category for alien organisms and both were low for species categorized as no known or low risk (range = 0.052–0.326) but were higher for species categorized as severe risk (range = 0.318–0.651). Selecting sites at random or manually did not improve the probability of finding novel alien species, but occupancy had a weak positive relationship with housing density for some categories of alien plants and insects. We used our empirical estimates to test alternative sampling designs that would minimize the combined variance of occupancy and detection (A-optimality criterion). Sampling designs with 8–10 visits per site were best for surveillance of new alien species if the probabilities of occupancy and detection were both low, and provided low conditional probabilities of site occupancy ($$\hat{\psi }_{condl}$$ ψ ^ condl ≤ 0.032) and a high probabilities of cumulative detection ($$\hat{p}*$$ p ^ ∗  ≥ 0.943). Our field results demonstrate that early detection is feasible as a key component of a national surveillance program based on early detection and rapid response.

Spatiotemporal relationships of coyotes and free-ranging domestic cats as indicators of conflict in Culver City, California

As habitat generalists, urban coyote (Canis latrans) populations often utilize an abundance of diverse food sources in cities. Within southern California, domestic cats (Felis catus) comprise a higher proportion of coyote diets than in other studied urban areas throughout the United States. However, it is unclear which ecological factors contribute to higher rates of cat depredation by coyotes in this region. While previous research suggests that coyote presence may have a negative effect on free-ranging domestic cat distributions, few studies have determined whether urban green spaces affect coyote or free-ranging domestic cat occurrence and activity within a predominantly urbanized landscape. We placed 20 remote wildlife cameras across a range of green spaces and residential sites in Culver City, California, an area of Los Angeles County experiencing pronounced coyote-domestic cat conflict. Using data collected across 6 months from 2019-2020, we assessed the influence of green space and prey species (i.e., cottontail rabbits (Sylvilagus spp.) and domestic cats) on coyote habitat use and activity. Coyotes exhibited a preference for sites with higher amounts of green space, while domestic cat habitat use was high throughout our study region. Although cottontail rabbit habitat use was also highly associated with urban green space, neither cottontails nor domestic cats appeared to temporally overlap significantly with coyotes. Unlike other cities where coyotes and domestic cats exhibit strong habitat partitioning across the landscape, domestic cats and coyotes spatially overlapped in green space fragments throughout Culver City. We suggest that this pattern of overlap may be responsible for the frequent cases of domestic cat depredation by coyotes in Culver City.

Optimizing management of invasions in an uncertain world using dynamic spatial models

Dispersal drives invasion dynamics of nonnative species and pathogens. Applying knowledge of dispersal to optimize the management of invasions can mean the difference between a failed and a successful control program and dramatically improve the return on investment of control efforts. A common approach to identifying optimal management solutions for invasions is to optimize dynamic spatial models that incorporate dispersal. Optimizing these spatial models can be very challenging because the interaction of time, space, and uncertainty rapidly amplifies the number of dimensions being considered. Addressing such problems requires advances in and the integration of techniques from multiple fields, including ecology, decision analysis, bioeconomics, natural resource management, and optimization. By synthesizing recent advances from these diverse fields, we provide a workflow for applying ecological theory to advance optimal management science and highlight priorities for optimizing the control of invasions. One of the striking gaps we identify is the extremely limited consideration of dispersal uncertainty in optimal management frameworks, even though dispersal estimates are highly uncertain and greatly influence invasion outcomes. In addition, optimization frameworks rarely consider multiple types of uncertainty (we describe five major types) and their interrelationships. Thus, feedbacks from management or other sources that could magnify uncertainty in dispersal are rarely considered. Incorporating uncertainty is crucial for improving transparency in decision risks and identifying optimal management strategies. We discuss gaps and solutions to the challenges of optimization using dynamic spatial models to increase the practical application of these important tools and improve the consistency and robustness of management recommendations for invasions.

Optimal invasive species surveillance in the real world: practical advances from research

When alien species make incursions into novel environments, early detection through surveillance is critical to minimizing their impacts and preserving the possibility of timely eradication. However, incipient populations can be difficult to detect, and usually, there are limited resources for surveillance or other response activities. Modern optimization techniques enable surveillance planning that accounts for the biology and expected behavior of an invasive species while exploring multiple scenarios to identify the most cost-effective options. Nevertheless, most optimization models omit some real-world limitations faced by practitioners during multi-day surveillance campaigns, such as daily working time constraints, the time and cost to access survey sites and personnel work schedules. Consequently, surveillance managers must rely on their own judgments to handle these logistical details, and default to their experience during implementation. This is sensible, but their decisions may fail to address all relevant factors and may not be cost-effective. A better planning strategy is to determine optimal routing to survey sites while accounting for common daily logistical constraints. Adding site access and other logistical constraints imposes restrictions on the scope and extent of the surveillance effort, yielding costlier but more realistic expectations of the surveillance outcomes than in a theoretical planning case.

Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty

Populations of invasive species often spread heterogeneously across a landscape, consisting of local populations that cluster in space but are connected by dispersal. A fundamental dilemma for invasive species control is how to optimally allocate limited fiscal resources across local populations. Theoretical work based on perfect knowledge of demographic connectivity suggests that targeting local populations from which migrants originate (sources) can be optimal. However, demographic processes such as abundance and dispersal can be highly uncertain, and the relationship between local population density and damage costs (damage function) is rarely known. We used a metapopulation model to understand how budget and uncertainty in abundance, connectivity, and the damage function, together impact return on investment (ROI) for optimal control strategies. Budget, observational uncertainty, and the damage function had strong effects on the optimal resource allocation strategy. Uncertainty in dispersal probability was the least important determinant of ROI. The damage function determined which resource prioritization strategy was optimal when connectivity was symmetric but not when it was asymmetric. When connectivity was asymmetric, prioritizing source populations had a higher ROI than allocating effort equally across local populations, regardless of the damage function, but uncertainty in connectivity structure and abundance reduced ROI of the optimal prioritization strategy by 57% on average depending on the control budget. With low budgets (monthly removal rate of 6.7% of population), there was little advantage to prioritizing resources, especially when connectivity was high or symmetric, and observational uncertainty had only minor effects on ROI. Allotting funding for improved monitoring appeared to be most important when budgets were moderate (monthly removal of 13-20% of the population). Our result showed that multiple sources of observational uncertainty should be considered concurrently for optimizing ROI. Accurate estimates of connectivity direction and abundance were more important than accurate estimates of dispersal rates. Developing cost-effective surveillance methods to reduce observational uncertainties, and quantitative frameworks for determining how resources should be spatially apportioned to multiple monitoring and control activities are important and challenging future directions for optimizing ROI for invasive species control programs.

Factors and costs associated with removal of a newly established population of invasive wild pigs in Northern U.S

The human-mediated spread of exotic and invasive species often leads to unintentional and harmful consequences. Invasive wild pigs (Sus scrofa) are one such species that have been repeatedly translocated throughout the United States and cause extensive damage to natural ecosystems, threatened and endangered species, agricultural resources, and private lands. In 2005, a newly established population of wild pigs was confirmed in Fulton County, Illinois, U.S. In 2011, a state-wide wild pig damage management program involving federal, state, and local government authorities directed a concerted effort to remove wild pigs from the county until the last wild pig (of 376 total) was successfully removed in 2016. We examined surveillance data from camera traps at bait sites and records of wild pig removals during this elimination program to identify environmental and anthropogenic factors that optimized removal of this population. Our results revealed that wild pigs used bait sites most during evening and nocturnal periods and on days with lower daily maximum temperatures. Increased removals of wild pigs coincided with periods of cold weather. We also identified that fidelity and time spent at bait sites by wild pigs was not influenced by increasing removals of wild pigs. Finally, the costs to remove wild pigs averaged $50 per wild pig (6.8 effort hours per wild pig) for removing the first 99% of the animals. Cost for removing the last 1% increased 84-fold, and averaged 122.8 effort hours per wild pig removed. Our results demonstrated that increased effort in removing wild pigs using bait sites should be focused during periods of environmental stress to maximize removal efficiency. These results inform elimination programs attempting to remove newly established populations of wild pigs, and ultimately prevent population and geographic expansion.

Reassessing the success of experts and nonexperts at correctly differentiating between closely related species from camera trap images: A reply to Gooliaff and Hodges

We present a reply to a recent article in Ecology and Evolution ("Measuring agreement among experts in classifying camera images of similar species" by Gooliaff and Hodges) that demonstrated a lack of consistency in expert-based classification of images of similar-looking species. We disagree with several conclusions from the study, and show that with some training, and use of multiple images that is becoming standard practice in camera-trapping studies, even nonexperts can identify similar sympatric species with high consistency.