The soft tick Ornithodoros turicata Duges (Acari: Argasidae) is a potential vector of African swine fever virus (ASFV). We evaluated the efficacy of two methods to collect soft ticks rapidly and efficiently from gopher tortoise (Gopherus polyphemus) burrows, which are ubiquitous throughout large regions of the southeastern United States and their burrows are a known microhabitat of O. turicata. Burrow vacuuming was an effective and efficient tick collection method; no tick was captured employing CO2 trapping. Using an occupancy modelling framework, we estimated that the probability of detecting ticks from an infested burrow each time a sample was taken with this method was 58% and increased with the average relative humidity. With the occupancy model, we estimated that 70% of the burrows in the study area were infested with O. turicata. Manual sifting of the burrow material yielded more ticks (6.6 individuals/sample) than using a set of three sieves (2.9 individuals/sample), yet the probability of detecting the species was not different between the two methods (Pval = 0.7). These methods can inform the development of ASF vector surveillance and outbreak response plans in areas of high risk for ASFV introduction in the region.

Monitoring programs are pivotal to establishing sound management. Due to economic, logistic, and time limitations, monitoring programs often overlook differences among life-history stages. However, species occurrence does not necessarily mean population viability, and it is unclear to what extent monitoring programs that do not consider separately adult presence and reproduction provide effective management indications. Unfortunately, collecting data on certain life stages requires high sampling effort, leading to a trade-off between model reliability and resources needed for monitoring. We collected data on presence and reproduction of amphibians by monitoring 207 waterbodies in Lombardy (northern Italy) in 2017-2022. We then used multistate occupancy models to test whether certain environmental features, namely, pond area, hydroperiod, forest cover, shade, aquatic vegetation, and predators\' presence, differentially affected adult occurrence and breeding probabilities of multiple amphibian species. To assess optimal sampling efforts, we modeled the detection probabilities of adults and reproduction across multiple species. Finally, we identified the optimal monitoring strategy under different scenarios of resource availability, comparing adult-only monitoring versus joint assessment of the occurrence of adults and reproduction. In many cases, the main drivers of adult occurrence and reproduction did not coincide because most investigated ecological variables affected one life stage or the other. Forest area, for instance, increased occurrence probabilities of adults of the endemic Rana latastei but showed no effect on their reproduction probabilities. Quantitative estimates of the sampling effort showed that occurrence of adults was easier to spot in 4 out of 7 species. Multicriteria decision analyses showed that when resources were scarce, monitoring adults was the optimal strategy for those 4 species. Conversely, with more resources, monitoring both adults and reproduction emerged as the best strategy for all the considered species. Integrated monitoring of adults and reproduction is essential to comprehensively identify effective conservation measures for amphibians.
Integración de datos de presencia adulta y de reproducción para identificar medidas de conservación para los anfibios Resumen Los programas de monitoreo son fundamentales para establecer una gestión adecuada. Debido a limitaciones económicas, logísticas y de tiempo, los programas de seguimiento suelen pasar por alto las diferencias entre las distintas fases del ciclo de vida. Sin embargo, la presencia de especies no significa necesariamente la viabilidad de la población, y no está claro hasta qué punto los programas de seguimiento que no consideran por separado la presencia de adultos y la reproducción proporcionan indicaciones eficaces para la gestión. Desgraciadamente, la recolección de datos sobre etapas determinadas del ciclo de vida requiere un gran esfuerzo de muestreo, lo que lleva a un compromiso entre la fiabilidad del modelo y los recursos necesarios para el seguimiento. Recopilamos datos sobre la presencia y reproducción de anfibios mediante el monitoreo de 207 cuerpos de agua en Lombardía (norte de Italia) entre 2017 y 2022. Después utilizamos modelos de ocupación multiestado para comprobar si determinadas características ambientales (superficie de los estanques, hidroperiodo, cubierta forestal, sombra, vegetación acuática y presencia de depredadores) afectaban de forma diferencial la presencia de adultos y a las probabilidades de reproducción de múltiples especies de anfibios. Para evaluar los esfuerzos de muestreo óptimos, modelamos las probabilidades de detección de adultos y reproducción de múltiples especies. Por último, identificamos la estrategia de seguimiento óptima en diferentes escenarios de disponibilidad de recursos y comparamos el seguimiento exclusivo de adultos frente a la evaluación conjunta de la presencia de adultos y reproducción. En muchos casos, los principales factores en la presencia de adultos y en la reproducción no coincidían, ya que la mayoría de las variables ecológicas investigadas afectaban a una u otra etapa del ciclo. La cubierta forestal, por ejemplo, aumentó la probabilidad de presencia de adultos de la especie endémica Rana latastei pero no mostraron un efecto sobre su probabilidad de reproducción. Las estimaciones cuantitativas del esfuerzo de muestreo mostraron que la presencia de adultos era más fácil de detectar en cuatro de las siete especies. Los análisis de decisión multicriterio mostraron que, cuando los recursos eran escasos, el seguimiento de los adultos era la estrategia óptima para esas cuatro especies. Por el contrario, con más recursos, el seguimiento tanto de los adultos como de la reproducción resultó ser la mejor estrategia para todas las especies consideradas. El seguimiento integrado de los adultos y la reproducción es esencial para identificar de forma exhaustiva medidas de conservación eficaces para los anfibios.
【摘要】 监测计划对于建立健全的管理制度至关重要。由于经济、后勤和时间等方面的限制, 监测计划往往忽略了生物不同生活史阶段之间的差异。然而, 物种出现并不一定意味着种群具有生存力, 因此目前还不清楚那些不单独考虑成体存在和繁殖的监测计划能在多大程度上提供有效的管理依据。遗憾的是, 收集某些生活史阶段的数据需要很大的取样工作量, 这就需要在模型可靠性和监测所需资源之间进行权衡。本研究通过2017‐2022年间对伦巴第(意大利北部)207个水体的监测, 收集了两栖动物的存在与繁殖数据。我们利用多状态占域模型检验了特定环境特征(即池塘面积、水文周期、森林覆盖率、遮荫度、水生植被和捕食者的存在)是否会对多种两栖动物的成体出现概率和繁殖概率产生不同影响。为了评估最佳取样力度, 我们建立了多个物种的成体出现和繁殖监测概率的模型。最后, 我们确定了不同资源可用性情景下的最佳监测策略, 比较了仅监测成体与综合评估成体出现和繁殖的情况。在许多情况下, 成体出现和繁殖的主要驱动因素并不一致, 因为大多数调查的生态变量仅会影响生活史的一个阶段。例如, 森林面积增加了特有种拉塔斯蛙(Rana latastei)的成体出现概率, 但对其繁殖概率没有影响。对取样力度的定量估计显示, 在七个物种中, 有四个物种的成体更容易被发现。多标准决策分析结果表明, 在资源稀缺的情况下, 监测成体是这四个物种的最佳监测策略。相反, 在资源较多的情况下, 同时监测成体和繁殖情况是对所有研究物种最佳的策略。我们提出, 整合成体出现和繁殖情况的监测对于全面确定两栖动物的有效保护措施至关重要。【翻译:胡怡思;审校:聂永刚】.

This commentary critiques the methodology, interpretation of results, and broader implications of a study by Jarčuška et al. (2024). We argue that the study\'s design and analysis fail to conclusively demonstrate any causal link between solar parks and bird diversity or community composition. Furthermore, focusing solely on species diversity and community composition, the study overlooks the importance of functional diversity and functional structure of communities in assessing the ecological impacts of solar parks on agricultural ecosystems. By exposing these shortcomings and recommending well-established methods for future research, we aim to ensure robust and informative studies that guide balanced decision-making for conservation and all stakeholders.

Quantifying the cost-effectiveness of alternative sampling methods is crucial for efficient biodiversity monitoring and detection of population trends. In this study, we compared the cost-effectiveness of three novel sampling methods for detecting changes in koala (Phascolarctos cinereus) occupancy: thermal drones, passive acoustic recorders and camera trapping. Specifically, we fitted single-season occupancy-detection models to data recorded from 46 sites in eight bioregions of New South Wales, Australia, between 2018 and 2022. We explored the effect of weather variables on daily detection probability for each method and, using these estimates, calculated the statistical power to detect 30%, 50% and 80% declines in koala occupancy. We calculated power for different combinations of sites (1-200) and repeat surveys (2-40) and developed a cost model that found the cheapest survey design that achieved 80% power to detect change. On average, detectability of koalas was highest with one 24-h period of acoustic surveys (0.32, 95% CI\'s: 0.26, 0.39) compared to a 25-ha flight of drone surveys (0.28, 95% 0.15, 0.48) or a 24-h period of camera trapping consisting of six cameras (0.019, 95% CI\'s: 0.014, 0.025). We found a negative quadratic relationship between detection probability and air temperature for all three methods. Our power and cost analysis suggested that 148 sites surveyed with acoustic recorders deployed for 14 days would be the cheapest method to sufficiently detect a 30% decline in occupancy with 80% power. We recommend passive acoustic recorders as the most efficient sampling method for monitoring koala occupancy compared to cameras or drones. Further comparative studies are needed to compare the relative effectiveness of these methods and others when the monitoring objective is to detect change in koala abundance over time.

The expansion of forest cover and intensification of agriculture represent the main threats to the bush cricket Saga pedo, currently listed as Vulnerable globally by the IUCN and included in Annex IV of the European Union Habitats Directive. Gathering information on its ecology and population size is challenging due to its low abundance and localized distribution. Additionally, the elusive and cryptic behavior of this species reduces the likelihood of its detection, potentially resulting in population underestimations. Thus, in this study, we aimed to (1) estimate S. pedo population size in relation to environmental variables and prey availability and (2) predict abundance of S. pedo in our study area for future monitoring in nearby territories. We found that the population of S. pedo in our study area consists of 197 (±115) individuals with a detection probability of 21.01% (±11.09). Detection probability of S. pedo further decreases on windy days. Moreover, we found that the investigated population of S. pedo occupies suboptimal areas, as highlighted not only by the predicted abundances but also by the association between S. pedo and other subfamilies of orthoptera that are ecologically very distant from our target species and mostly linked to mesophilic biotopes. Most of the individuals we observed are concentrated in small clearings completely within wooded matrices and therefore isolated from each other. Based on our results, it is possible that forest expansion toward open meadows represents the main threat to this population, transforming the clearings and xeric meadows (to which S. pedo is linked) into small and fragmented patches that are suboptimal and insufficient to host viable populations.

Ixodid (hard) ticks play important ecosystem roles and have significant impacts on animal and human health via tick-borne diseases and physiological stress from parasitism. Tick occurrence, abundance, activity, and key life-history traits are highly influenced by host availability, weather, microclimate, and landscape features. As such, changes in the environment can have profound impacts on ticks, their hosts, and the spread of diseases. Researchers recognize that spatial and temporal factors influence activity and abundance and attempt to account for both by conducting replicate sampling bouts spread over the tick questing period. However, common field methods notoriously underestimate abundance, and it is unclear how (or if) tick studies model the confounding effects of factors influencing activity and abundance. This step is critical as unaccounted variance in detection can lead to biased estimates of occurrence and abundance. We performed a descriptive review to evaluate the extent to which studies account for the detection process while modeling tick data. We also categorized the types of analyses that are commonly used to model tick data. We used hierarchical models (HMs) that account for imperfect detection to analyze simulated and empirical tick data, demonstrating that inference is muddled when detection probability is not accounted for in the modeling process. Our review indicates that only 5 of 412 (1 %) papers explicitly accounted for imperfect detection while modeling ticks. By comparing HMs with the most common approaches used for modeling tick data (e.g., ANOVA), we show that population estimates are biased low for simulated and empirical data when using non-HMs, and that confounding occurs due to not explicitly modeling factors that influenced both detection and abundance. Our review and analysis of simulated and empirical data shows that it is important to account for our ability to detect ticks using field methods with imperfect detection. Not doing so leads to biased estimates of occurrence and abundance which could complicate our understanding of parasite-host relationships and the spread of tick-borne diseases. We highlight the resources available for learning HM approaches and applying them to analyzing tick data.

Population density and structure are critical to nature conservation and pest management. Traditional sampling methods such as capture-mark-recapture and catch-effort can\'t be used in situations where catching, marking, or removing individuals are not feasible. N-mixture models use repeated count data to estimate population abundance based on detection probability. They are widely adopted in wildlife surveys in recent years to account for imperfect detection. However, its application in entomology is relatively new. In this paper, we describe the general procedures of N-mixture models in population studies from data collection to model fitting and evaluation. Using Lycorma delicatula egg mass survey data at 28 plots in seven sites from the field, we found that detection probability (p) was negatively correlated with tree diameter at breast height (DBH), ranged from 0.516 [95 % CI: 0.470-0.561] to 0.614 [95 % CI: 0.566-0.660] between the 1st and the 3rd sample period. Furthermore, egg mass abundance (λ) was positively associated with basal area (BA) for the sample unit (single tree), with more egg masses on tree of heaven (TOH) trees. More egg masses were also expected on trees of other species in TOH plots. Predicted egg mass density (masses/100 m2) ranged from 5.0 (95 % CI: 3.0-16.0) (Gordon) to 276.9 (95 % CI: 255.0-303.0) (Susquehannock) for TOH plots, and 11.0 (95 % CI: 9.00-15.33) (Gordon) to 228.3 (95 % CI: 209.7-248.3) (Burlington) for nonTOH plots. Site-specific abundance estimates from N-mixture models were generally higher compared to observed maximum counts. N-mixture models could have great potential in insect population surveys in agriculture and forestry in the future.

Predicting the spatial occurrence of wildlife is a major challenge for ecology and management. In Latin America, limited knowledge of the number and locations of vampire bat roosts precludes informed allocation of measures intended to prevent rabies spillover to humans and livestock. We inferred the spatial distribution of vampire bat roosts while accounting for observation effort and environmental effects by fitting a log Gaussian Cox process model to the locations of 563 roosts in three regions of Peru. Our model explained 45% of the variance in the observed roost distribution and identified environmental drivers of roost establishment. When correcting for uneven observation effort, our model estimated a total of 2340 roosts, indicating that undetected roosts (76%) exceed known roosts (24%) by threefold. Predicted hotspots of undetected roosts in rabies-free areas revealed high-risk areas for future viral incursions. Using the predicted roost distribution to inform a spatial model of rabies spillover to livestock identified areas with disproportionate underreporting and indicated a higher rabies burden than previously recognized. We provide a transferrable approach to infer the distribution of a mostly unobserved bat reservoir that can inform strategies to prevent the re-emergence of an important zoonosis.

There are many advantages to transitioning from conducting marine wildlife surveys via human observers onboard light-aircraft, to capturing aerial imagery using drones. However, it is important to maintain the validity of long-term data series whilst transitioning from observer to imagery surveys. We need to understand how the detection rates of target species in images compare to those collected from observers in piloted aircraft, and the factors influencing detection rates from each platform. We conducted trial ScanEagle drone surveys of dugongs in Shark Bay, Western Australia, covering the full extent of the drone\'s range (∼100 km), concurrently with observer surveys, with the drone flying above or just behind the piloted aircraft. We aimed to test the assumption that drone imagery could provide comparable detection rates of dugongs to human observers when influenced by same environmental conditions. Overall, the dugong sighting rate (i.e., count of individual dugongs) was 1.3 (95% CI [0.98-1.84]) times higher from the drone images than from the observers. The group sighting rate was similar for the two platforms, however the group sizes detected within the drone images were significantly larger than those recorded by the observers, which explained the overall difference in sighting rates. Cloud cover appeared to be the only covariate affecting the two platforms differently; the incidence of cloud cover resulted in smaller group sizes being detected by both platforms, but the observer group sizes dropped much more dramatically (by 71% (95% CI [31-88]) compared to no cloud) than the group sizes detected in the drone images (14% (95% CI [-28-57])). Water visibility and the Beaufort sea state also affected dugong counts and group sizes, but in the same way for both platforms. This is the first direct simultaneous comparison between sightings from observers in piloted aircraft and a drone and demonstrates the potential for drone surveys over a large spatial-scale.

Camera trap data are biased when an animal passes through a camera\'s field of view but is not recorded. Cameras that operate using passive infrared sensors rely on their ability to detect thermal energy from the surface of an object. Optimal camera deployment consequently depends on the relationship between a sensor array and an animal. Here, we describe a general, experimental approach to evaluate detection errors that arise from the interaction between cameras and animals. We adapted distance sampling models and estimated the combined effects of distance, camera model, lens height, and vertical angle on the probability of detecting three different body sizes representing mammals that inhabit temperate, boreal, and arctic ecosystems. Detection probabilities were best explained by a half-normal-logistic mixture and were influenced by all experimental covariates. Detection monotonically declined when proxies were ≥6 m from the camera; however, models show that body size and camera model mediated the effect of distance on detection. Although not a focus of our study, we found that unmodeled heterogeneity arising from solar position has the potential to bias inferences where animal movements vary over time. Understanding heterogeneous detection probabilities is valuable when designing and analyzing camera trap studies. We provide a general experimental and analytical framework that ecologists, citizen scientists, and others can use and adapt to optimize camera protocols for various wildlife species and communities. Applying our framework can help ecologists assess trade-offs that arise from interactions among distance, cameras, and body sizes before committing resources to field data collection.