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Here we assessed population dynamics, natural enemy fauna (with emphasis on egg parasitoid), and population genetic structure (based on mitochondrial DNA) of the invasive litchi stink bug (LSB), Tessaratoma papillosa in Taiwan. Our major findings include: (1) fluctuations of LSB in numbers of adults, mating pairs, and egg masses over a 2-year period in Taiwan generally resemble those in the native populations; (2) Anastatusdexingensis and A. fulloi are among the most dominant LSB egg parasitoids, with the former consistently outnumbering the latter throughout Taiwan; (3) the presence of two genetically distinct clades suggests LSB in Taiwan most likely derived from multiple invasions. All these data practically improve our understanding of this invasive insect pest, particularly its ecological and genetic characteristics in the introduced area, which represents critical baseline information for the design of future integrated pest management strategies.

Lemmings are a key component of tundra food webs and changes in their dynamics can affect the whole ecosystem. We present a comprehensive overview of lemming monitoring and research activities, and assess recent trends in lemming abundance across the circumpolar Arctic. Since 2000, lemmings have been monitored at 49 sites of which 38 are still active. The sites were not evenly distributed with notably Russia and high Arctic Canada underrepresented. Abundance was monitored at all sites, but methods and levels of precision varied greatly. Other important attributes such as health, genetic diversity and potential drivers of population change, were often not monitored. There was no evidence that lemming populations were decreasing in general, although a negative trend was detected for low arctic populations sympatric with voles. To keep the pace of arctic change, we recommend maintaining long-term programmes while harmonizing methods, improving spatial coverage and integrating an ecosystem perspective.

Estimating the abundance of organisms is fundamental to the study and management of ecological systems. However, accurately and precisely estimating organism abundance is challenging, especially in aquatic systems where organisms are hidden underwater. Estimating the abundance of fish is critical for the management of fisheries which relies on accurate assessment of population status to maximize yield without overharvesting populations. Monitoring population status is particularly challenging for inland fisheries in which populations are distributed among many individual waterbodies. Environmental DNA (eDNA) may offer a cost‐effective way to rapidly estimate populations across a large number of systems if eDNA quantity correlates with the abundance of its source organisms. Here, we test the ability of quantities of eDNA recovered from surface water to estimate the abundance of walleye (Sander vitreus), a culturally and economically important sportfish, in lakes in northern Wisconsin (USA). We demonstrate a significant, positive relationship between traditional estimates of adult walleye populations (both number of individuals and biomass) and eDNA concentration (R2 = .81; n = 22). Our results highlight the utility of eDNA as a population monitoring tool that can help guide and inform inland fisheries management. Quantity of eDNA predicts abundance of an economically important sport fish (Sander vitreus) in inland lakes of northern Wisconsin (USA; R2 = .81). These results highlight the utility of eDNA as a population monitoring tool that can help guide and inform inland fisheries management.

Large carnivores are elusive and use large areas, which causes monitoring to be challenging and costly. Moreover, management to reduce conflicts and simultaneously ensure long-term population viability require precise population estimates. In Scandinavia, the monitoring of wolves (Canis lupus) is primarily based on counting packs, identifying reproduction, and genetically identifying territorial wolves from noninvasive DNA samples. We assessed the reliability of wolf monitoring in Scandinavia by estimating the detectability of territorial pairs, packs, and reproduction. Our data, comprising snow-tracking data and DNA-identified individuals from 2005–2016, covered 11 consecutive winter monitoring seasons (Oct–Mar). Among 343 cases where we identified a wolf pack, territorial wolves were also detected in the same area during the previous season in 323 (94.2%) cases. In only 6 of the remaining 20 cases, there was no prior knowledge of territorial wolves in the area. Among the 328 detected reproduction events (litter born to a pack), we detected 97% during the monitoring period and identified the rest ≥1 year later from kinship assessments of all DNA-detected individuals. These results suggest that we failed to detect only few packs with reproduction events during the monitoring season that followed breeding. Yearly monitoring of territorial individuals and continuous updates of the pedigree allowed us to retrospectively identify reproduction events and packs that were not identified earlier.

Monitoring large herbivores across their core range has been readily accomplished using aerial surveys and traditional distance sampling. But for peripheral populations, where individuals may occur in patchy, low-density populations, precise estimation of population size and trend remains logistically and statistically challenging. For moose (Alces alces) along their southern range margin in northern New York, USA, we sought robust estimates of moose distribution, abundance, and population trend (2016–2019) using a combination of aerial surveys (line transect distance-sampling), repeated surveys in areas where moose were known to occur to boost the number of detections, and density surface modeling (DSM) with spatial covariates. We achieved a precise estimate of density (95% CI = 0.00–0.29 moose/km²) for this small population (656 moose, 95% CI = 501–859), which was patchily distributed across a large and heavily forested region (the 24,280-km² Adirondack Park). Local moose abundance was positively related to active timber management, elevation, and snow cover, and negatively related to large bodies of water. As expected, moose abundance in this peripheral population was low relative to its core range in other northern forest states. Yet, in areas where abundance was greatest, moose densities in New York approached those where epizootics of winter tick (Dermacentor albipictus) have been reported, underscoring the need for effective and efficient monitoring. By incorporating autocorrelation in observations and landscape covariates, DSM provided spatially explicit estimates of moose density with greater precision and no additional field effort over traditional distance sampling. Combined with repeated surveys of areas with known moose occurrence to achieve viable sample sizes, DSM is a useful tool for effectively monitoring low density and patchy populations.

Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High‐resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status. We present a model that accounts for false negative (missed plants) and false positive (misclassified or double‐counted plants) error in counts from high‐resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post‐wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating >16 ha of UAS imagery with >700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance. Raw counts of plant abundance in high‐resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting (p) adult plants (≥0.25 m tall) varied between sites within 0.52 < p̂adult < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < p̂small < 0.3. On average, we estimate that 19% of all detected plants were false positive errors, which varied spatially in relation to topographic predictors. Abundance declined toward the interior of previous wildfires and was positively associated with terrain roughness. Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. We believe such models will further improve inference on plant population dynamics—relevant to restoration, wildlife habitat and related objectives—and echo previous calls for remote sensing applications to better differentiate between ecological and observational processes.

1. Efficient and reliable methods for estimating animal density are essential to wildlife conservation and management. Camera trapping is an increasingly popular tool in this area of wildlife research, with further potential arising from technological improvements, such as video-recording functions that allow for behavioural observation of animals. This information may be useful in the estimation of animal density, even without individual recognition. Although several models applicable to species lacking individual markings (i.e. unmarked populations) have been developed, a methodology incorporating behavioural information from videos has not yet been established. 2. We developed a likelihood-based model: the random encounter and staying time (REST) model. It is an extension of the random encounter model by Rowcliffe et al. (J Appl Ecol 45:1228, 2008). The REST model describes the relationship among staying time, trapping rate, and density, which is estimable using a frequentist or Bayesian approach. We tested the reliability and feasibility of the REST model using Monte Carlo simulations. We also applied the approach in the African rainforest and compared the results with those of a line-transect survey. 3. The simulations showed that the REST model provided unbiased estimates of animal density. Even when animal movement speeds varied among individuals, and when animals travelled in pairs, the model provided unbiased density estimates. However, the REST model was vulnerable to unsynchronized activity patterns among individuals. Moreover, it is necessary to use a camera model with a fast and reliable infrared sensor and to set the camera trap's parameters appropriately (i.e. video length, delay period). The field survey showed that the staying time of two ungulate species in the African rainforest exhibited good fit with a temporal parametric distribution, and the REST model provided density estimates consistent with those of a line-transect survey. 4. Synthesis and applications. The random encounter and staying time model provides better efficiency and higher feasibility than the random encounter model in estimating animal density without individual recognition. Careful application of the random encounter and staying time model provides the potential to estimate density of many ground-dwelling vertebrates lacking individually recognizable markings, and thus should be an effective method for population monitoring.

Apex carnivores are wide‐ranging, low‐density, hard to detect, and declining throughout most of their range, making population monitoring both critical and challenging. Rapid and inexpensive index calibration survey (ICS) methods have been developed to monitor large African carnivores. ICS methods assume constant detection probability and a predictable relationship between the index and the actual population of interest. The precision and utility of the resulting estimates from ICS methods have been questioned. We assessed the performance of one ICS method for large carnivores—track counts—with data from two long‐term studies of African lion populations. We conducted Monte Carlo simulation of intersections between transects (road segments) and lion movement paths (from GPS collar data) at varying survey intensities. Then, using the track count method we estimated population size and its confidence limits. We found that estimates either overstate precision or are too imprecise to be meaningful. Overstated precision stemmed from discarding the variance from population estimates when developing the method and from treating the conversion from tracks counts to population density as a back‐transformation, rather than applying the equation for the variance of a linear function. To effectively assess the status of species, the IUCN has set guidelines, and these should be integrated in survey designs. We propose reporting the half relative confidence interval width (HRCIW) as an easily calculable and interpretable measure of precision. We show that track counts do not adhere to IUCN criteria, and we argue that ICS methods for wide‐ranging low‐density species are unlikely to meet those criteria. Established, intensive methods lead to precise estimates, but some new approaches, like short, intensive, (spatial) capture–mark–recapture (CMR/SECR) studies, aided by camera trapping and/or genetic identification of individuals, hold promise. A handbook of best practices in monitoring populations of apex carnivores is strongly recommended. We tested and analyzed a popular rapid and cheap track survey method for large carnivores with randomly generated transects and empirical data on lion movements. We found that these methods are not able to reliably detect changes in populations of large carnivores often not even including the true population size in its confidence interval. We argue that the power of population estimates should meet criteria, preferably the IUCN guidelines, to detect population trends.

Hawaii has experienced profound declines in native avifauna alongside the introduction of numerous bird species. While site‐specific population studies are common, landscape‐level analyses of avian population dynamics are rare, particularly in island ecosystems. To address this gap, we used a density surface model to create a spatio‐temporal projection of population densities and distributions across the Island of Hawai‘i, spanning nearly five decades (1976–2023). We incorporated environmental covariates of habitat, precipitation, and elevation, to further refine our projections. Our analysis encompassed nine native and six non‐native bird species, inhabiting a range of ecological niches. We found five out of nine native species have declined in density and range size while four were stable. For non‐native species, two were stable, one was decreasing, and three were increasing in density and range size. Our landscape projections can inform management by suggesting areas critical for habitat preservation and land acquisition for conservation, identifying where range fragmentation is occurring, and pinpointing locations of multi‐species declines that are likely driven by a common cause. Our study demonstrates how long‐term, landscape‐level monitoring and analyses can advance understanding and addressing biodiversity loss, particularly in vulnerable tropical island ecosystems.

Bird surveys conducted using aerial images can be more accurate than those using airborne observers, but can also be more time-consuming if images must be analyzed manually. Recent advances in digital cameras and image-analysis software offer unprecedented potential for computer-automated bird detection and counts in high-resolution aerial images. We review the literature on this subject and provide an overview of the main image-analysis techniques. Birds that contrast sharply with image backgrounds (e.g., bright birds on dark ground) are generally the most amenable to automated detection, in some cases requiring only basic imageanalysis software. However, the sophisticated analysis capabilities of modern object-based image analysis software provide ways to detect birds in more challenging situations based on a variety of attributes including color, size, shape, texture, and spatial context. Some techniques developed to detect mammals may also be applicable to birds, although the prevalent use of aerial thermal-infrared images for detecting large mammals is of limited applicability to birds because of the low pixel resolution of thermal cameras and the smaller size of birds. However, the increasingly high resolution of true-color cameras and availability of small unmanned aircraft systems (drones) that can fly at very low altitude now make it feasible to detect even small shorebirds in aerial images. Continued advances in camera and drone technology, in combination with increasingly sophisticated image analysis software, now make it possible for investigators involved in monitoring bird populations to save time and resources by increasing their use of automated bird detection and counts in aerial images. We recommend close collaboration between wildlife-monitoring practitioners and experts in the fields of remote sensing and computer science to help generate relevant, accessible, and readily applicable computer-automated aerial photographic census techniques. Conteo de aves realizados utilizando imágenes aéreas pueden ser mas precisos que los que utilizan observadores desde el aire, pero pueden consumir mas tiempo si las imágenes tienen que ser analizadas manualmente. Avances recientes en cámaras digitales y software de análisis de imágenes ofrecen un potencial sin precedentes para la detección computacional automática de aves y conteos en imágenes aéreas de alta resolución. Revisamos la literatura en este tema y ofrecemos una visión general de las principales técnicas de análisis en imágenes. Las aves que tienen un fuerte contraste con los fondos de las imágenes (e.g., aves brillantes en fondos oscuros) son en general las mas sensibles a las detecciones automáticas, en algunos casos solo requieren un software básico de analizador de imágenes. Sin embargo, las sofisticadas capacidades de los software de análisis modernos en imágenes basadas en objetos, proveen formas de detectar aves en situaciones mas desafiantes basadas en una variedad de atributos incluyendo el color, tamaño, forma, textura y contexto espacial. Algunas técnicas desarrolladas para detectar mamíferos pueden ser aplicables en aves, aunque el uso predominante de imágenes aéreas de infra rojo térmico para detectar grandes mamíferos tienen aplicabilidad limitada para las aves, debido a la baja resolución en los pixeles de las cámaras térmicas y el tamaño pequeño de las aves. Sin embargo, el incremento en la alta resolución de las cámaras de color y la disponibilidad de pequeños sistemas de aeronaves no tripuladas (drones) que pueden volar a bajas elevaciones, ahora hacen que sea posible detectar incluso pequeñas aves playeras en imágenes aéreas. Los continuos avances en la tecnología de la cámara y aeronaves no tripuladas, en combinación con software de análisis de imágenes cada vez más sofisticados, ahora hacen posible ahorrara tiempo y recursos a los investigadores involucrados en el monitoreo de las poblaciones de aves, mediante el aumento del uso de la detección y conteos de aves automatizado en imágenes aéreas. Recomendamos una estrecha colaboración entre los profesionales de monitoreo de fauna silvestre y expertos en el campo de la teledetección y la informática para ayudar a generar técnicas de censo relevantes, accesibles y de fácil aplicación automatizada computacionalmente utilizando fotografías aéreas.