Timing and technique impact the effectiveness of road‐based,mobile acoustic surveys of bats

Mobile acoustic surveys are a common method of surveying bat communities. However, there is a paucity of empirical studies exploring different methods for conducting mobile road surveys of bats. During 2013, we conducted acoustic mobile surveys on three routes in north‐central Indiana, U.S.A., using (1) a standard road survey, (2) a road survey where the vehicle stopped for 1 min at every half mile of the survey route (called a “start‐stop method”), and (3) a road survey with an individual using a bicycle. Linear mixed models with multiple comparison procedures revealed that when all bat passes were analyzed, using a bike to conduct mobile surveys detected significantly more bat passes per unit time compared to other methods. However, incorporating genus‐level comparisons revealed no advantage to using a bike over vehicle‐based methods. We also found that survey method had a significant effect when analyses were limited to those bat passes that could be identified to genus, with the start–stop method generally detecting more identifiable passes than the standard protocol or bike survey. Additionally, we found that significantly more identifiable bat passes (particularly those of the Eptesicus and Lasiurus genera) were detected in surveys conducted immediately following sunset. As governing agencies, particularly in North America, implement vehicle‐based bat monitoring programs, it is important for researchers to understand how variations on protocols influence the inference that can be gained from different monitoring schemes.     

Satellite Telemetry in Avian Research and Management: Sample Size Considerations

Abstract: Satellite tracking is currently used to make inferences to avian populations. Cost of transmitters and logistical challenges of working with some species can limit sample size and strength of inferences. Therefore, careful study design including consideration of sample size is important. We used simulations to examine how sample size, population size, and population variance affected probability of making reliable inferences from a sample and the precision of estimates of population parameters. For populations of >100 individuals, a sample >20 birds was needed to make reliable inferences about questions with simple outcomes (i.e., 2 possible outcomes). Sample size demands increased rapidly for more complex problems. For example, in a problem with 3 outcomes, a sample of >75 individuals will be needed for proper inference to the population. Combining data from satellite telemetry studies with data from surveys or other types of sampling may improve inference strength.     

Simulations inform design of regional occupancy‐based monitoring for a sparsely distributed,territorial species

Sparsely distributed species attract conservation concern, but insufficient information on population trends challenges conservation and funding prioritization. Occupancy‐based monitoring is attractive for these species, but appropriate sampling design and inference depend on particulars of the study system. We employed spatially explicit simulations to identify minimum levels of sampling effort for a regional occupancy monitoring study design, using white‐headed woodpeckers (Picoides albolvartus), a sparsely distributed, territorial species threatened by habitat decline and degradation, as a case study. We compared the original design with commonly proposed alternatives with varying targets of inference (i.e., species range, space use, or abundance) and spatial extent of sampling. Sampling effort needed to achieve adequate power to observe a long‐term population trend (≥80% chance to observe a 2% yearly decline over 20 years) with the previously used study design consisted of annually monitoring ≥120 transects using a single‐survey approach or ≥90 transects surveyed twice per year using a repeat‐survey approach. Designs that shifted inference toward finer‐resolution trends in abundance and extended the spatial extent of sampling by shortening transects, employing a single‐survey approach to monitoring, and incorporating a panel design (33% of units surveyed per year) improved power and reduced error in estimating abundance trends. In contrast, efforts to monitor coarse‐scale trends in species range or space use with repeat surveys provided extremely limited statistical power. Synthesis and applications. Sampling resolutions that approximate home range size, spatially extensive sampling, and designs that target inference of abundance trends rather than range dynamics are probably best suited and most feasible for broad‐scale occupancy‐based monitoring of sparsely distributed territorial animal species.     

Complete allocation sampling: an efficient and easily implemented adaptive sampling design

Adaptive sampling designs are becoming increasingly popular in environmental science, particularly for surveying rare and aggregated populations. An adaptive sample is one in which the survey design is modified, or adapted, in some way on the basis of information gained during the survey. There are many different adaptive survey designs that can be used to estimate animal and plant abundance. In adaptive cluster sampling, additional sample effort is allocated during the survey to the immediate neighborhood in which the species is found. In adaptive stratified sampling, additional sample effort is allocated during the survey to strata of high abundance. The appealing feature of these adaptive designs is that the field biologist gets to do what innately seems sensible when working with rare and aggregated populations—field effort is targeted around where the species is observed in the first wave of the survey. However, there are logistical challenges of applying this principle of targeted field effort while remaining in the framework of probability-based sampling. We propose a simplified adaptive survey design that incorporates both targeting field effort and being logistically feasible. We show with a case study population of rockfish that complete allocation stratified sampling is a very efficient design.     

Optimizing cost-efficiency of long term monitoring programs by using spatially balanced sampling designs: The case of manila clams in Arcachon bay

Lack of funds is one major issue in ecology, in particular at local scale. It is known that sustainable management of a natural population requires a good understanding of its functioning, itself dependent on a good long term monitoring program. Such programs are usually very difficult to implement, especially for resources characterized by high spatio-temporal variation in their distribution, resulting in a trade off between efficiency and costs. Today, thanks to rapidly evolving statistical theory, new survey designs are developed, some with the characteristic of well balancing samples in the study area. This paper aims at demonstrating that theses advanced sampling designs perform better than the usual ones for long term monitoring program of local resources, with the added benefices of saving money and also increasing results accuracy. To prove it, and for it high spatio-temporal variation in it distribution, we choose the example of Manila clam's stock monitoring in Arcachon bay. This stock is under high scrutiny and last campaigns could not be done because of lack of funding (at least 50,000€/survey). We use a simulation study based on real data to assess and compare performances of news and older sampling designs on this survey. Three sampling designs are tested in both of the 6 past monitoring campaigns data and we estimate the cost of their application in the field. Selected sampling designs are: 1 - simple random sampling (SRS - the one used in the past years of this monitoring program), 2 - generalized tessellation sampling (GRTS - a recent spatially balanced sampling design known for its high performance) and, 3 - balanced acceptance sampling design (BAS - a newly developed spatially balanced sampling design, never tested yet in a real population). We first confirm that the two spatially balanced sampling designs perform better than simple random sampling. Both of the advanced sampling designs perform equally and allow achieving same accuracy in the results with almost half sampling intensity than SRS. This makes them so cost-effective that 30% of each campaign price could be saved if they were used. Moreover, the three sampling designs need a constant sample size thought years to achieve a fixed accuracy in results. This will permit us to fix one sample size that could be done for all future campaigns; and this, despite the existence of spatial and temporal variations in clam's distribution.     

Improving inference for aerial surveys of bears: The importance of assumptions and the cost of unnecessary complexity

Obtaining useful estimates of wildlife abundance or density requires thoughtful attention to potential sources of bias and precision, and it is widely understood that addressing incomplete detection is critical to appropriate inference. When the underlying assumptions of sampling approaches are violated, both increased bias and reduced precision of the population estimator may result. Bear (Ursus spp.) populations can be difficult to sample and are often monitored using mark‐recapture distance sampling (MRDS) methods, although obtaining adequate sample sizes can be cost prohibitive. With the goal of improving inference, we examined the underlying methodological assumptions and estimator efficiency of three datasets collected under an MRDS protocol designed specifically for bears. We analyzed these data using MRDS, conventional distance sampling (CDS), and open‐distance sampling approaches to evaluate the apparent bias‐precision tradeoff relative to the assumptions inherent under each approach. We also evaluated the incorporation of informative priors on detection parameters within a Bayesian context. We found that the CDS estimator had low apparent bias and was more efficient than the more complex MRDS estimator. When combined with informative priors on the detection process, precision was increased by >50% compared to the MRDS approach with little apparent bias. In addition, open‐distance sampling models revealed a serious violation of the assumption that all bears were available to be sampled. Inference is directly related to the underlying assumptions of the survey design and the analytical tools employed. We show that for aerial surveys of bears, avoidance of unnecessary model complexity, use of prior information, and the application of open population models can be used to greatly improve estimator performance and simplify field protocols. Although we focused on distance sampling‐based aerial surveys for bears, the general concepts we addressed apply to a variety of wildlife survey contexts.     

Hierarchical multi-scale occupancy estimation for monitoring wildlife populations

Occupancy estimation is an effective analytic framework, but requires repeated surveys of a sample unit to estimate the probability of detection. Detection rates can be estimated from spatially replicated rather than temporally replicated surveys, but this may violate the closure assumption and result in biased estimates of occupancy. We present a new application of a multi-scale occupancy model that permits the simultaneous use of presence–absence data collected at 2 spatial scales and uses a removal design to estimate the probability of detection. Occupancy at the small scale corresponds to local territory occupancy, whereas occupancy at the large scale corresponds to regional occupancy of the sample units. Small-scale occupancy also corresponds to a spatial availability or coverage parameter where a species may be unavailable for sampling at a fraction of the survey stations. We applied the multi-scale occupancy model to a hierarchical sample design for 2 bird species in the Black Hills National Forest: brown creeper (Certhia americana) and lark sparrow (Chondestes grammacus). Our application of the multi-scale occupancy model is particularly well suited for hierarchical sample designs, such as spatially replicated survey stations within sample units that are typical of avian monitoring programs. The model appropriately accounts for the non-independence of the spatially replicated survey stations, addresses the closure assumption for the spatially replicated survey stations, and is useful for decomposing the observation process into detection and availability parameters. This analytic approach is likely to be useful for monitoring at local and regional scales, modeling multi-scale habitat relationships, and estimating population state variables for rare species of conservation concern. © 2011 The Wildlife Society.     

Optimizing ultrasonic sampling effort for monitoring forest bats

Landscape‐scale monitoring is a key approach for assessing changes in indicators. However, great care needs to be taken to collect rigorous data and avoid wasting resources in long‐term programmes. Insect‐eating bats are diverse, functionally important and are often proposed as indicator species of environmental health. We used acoustic (ultrasonic) data from pilot bat surveys undertaken in forests and woodlands to optimize sampling effort to produce precise estimates of bat activity and occupancy. We also carried out simulations to evaluate the statistical power of different sampling designs to detect changes in activity and occupancy levels of individual bat species. There was little gain in precision for estimates of bat activity by sampling beyond five to six detector nights. To ensure spatial heterogeneity was sampled around a monitoring point, three detectors for two nights or two detectors for three nights would be required. This level of sampling was also sufficient to be 90% certain of recording occupancy for 11 of 12 taxa. Power simulations revealed that a sampling design using two detectors per monitoring point for two nights could detect a 30% decline within 10 years with 90% power for all species, except the white‐striped free tail bat (Tadarida australis), using either changes in activity levels or occupancy. However, fewer years were required when using occupancy. Setting detectors either on‐flyways or off‐flyways contributed only minor differences to the time taken to reach 90% power for both occupancy and activity levels, though sampling both locations has major implications for interpreting trends in bats. We suggest that bat activity levels are more sensitive for detecting change than occupancy because one pass or 1000 passes can be recorded per night by an acoustic detector, and this is not differentiated by occupancy. Bats can be monitored cost‐effectively and should be included in monitoring programmes.     

Sampling designs, field techniques and analytical methods for systematic plant population surveys

Summary Intense pressures on the use and management of land underscore the need for reliable and up-to-date information on the status of native species. The outcomes of the most recent plant population surveys commissioned by agencies are generally limited by faults or omissions in survey design. There is little guidance on how to design and implement field surveys of plant populations in ways that address the most pertinent gaps in our current knowledge and provide answers of known reliability. In this paper, I used the International Union for the Conservation of Nature (IUCN) Red List criteria as a framework to define the data required from surveys to assess the conservation status of potentially threatened species. The criteria address the location and geographical range of extant populations, aspects of species' life history, the size and structure of extant populations and rates of change in abundance and range. I have described survey designs and sampling techniques for estimating these parameters. Choices of appropriate methods that consider trade-offs between desired levels of precision and rigour and sampling effort are illustrated using surveys of 13 Tasmanian Epacris species as examples. Key elements of the approach are: (i) systematic approaches to field searches and recording both positive and negative search outcomes; (ii) construction and testing of intuitive or quantitative distribution models in an explicit experimental framework; (iii) rigorous cost-effective sampling designs, systematic field methodologies and simple analytical techniques to estimate both the magnitude and uncertainty of distribution and abundance; (iv) assessment of the merits and limitations of alternative sampling options; and (v) inference of changes in distribution and abundance by judicious use of historical data and field evidence of recent population processes.     

Searching for weeds – a GIS tool

Sampling for rare events, such as a new weed incursion, can be surprisingly efficient when adaptive, unequal probability survey designs are used. Spatially explicit habitat models and expert knowledge of weedy species can be used to identify areas of varied survey intensity. We introduce a GIS-based tool that can be used for designing such a survey. The user-friendly tool interfaces (behind the scenes) with the US Environmental Protection Agency’s spatially balanced sampling design functions in R. The functions ensure that the location of the sample points are spatially balanced while at the same time, allowing the user to specify survey intensity in area of special interest (preferred habitats, areas of high conservation value, areas of high public use, etc). We discuss the use of the GIS tool in a case study where we designed a 5-year weed monitoring plan for a local authority in New Zealand. The plan includes ‘over sample’ sites to replace any original sample sites that were impractical or costly to visit. Initial results include estimates of what proportion of the total region has weeds present and an estimate of weed density. More detailed results are produced for specified known weed hot spots, such as areas adjacent to roads and rivers. These estimates are available for all weed species, and for individual species. Because the system is GIS-based, spatial information is stored. Over time, as the weed surveillance and monitoring progresses, regional changes in weed distribution can be tracked, and species and locations that require more targeted weed management can be identified. Further results of such a probability-based design can be used to develop habitat models for predicting future distributions.     

Patterns of activity and fatality of migratory bats at a wind energy facility in Alberta,Canada

Studying migratory behavior of bats is challenging. Thus, most information regarding their migratory behavior is anecdotal. Recently, however, fatalities of migratory bats at some wind energy facilities across North America have provided the opportunity and impetus to study bat migration at fine spatial and temporal scales. Using acoustic monitoring and carcass searches, we examined temporal and spatial variation in activity levels and fatality rates of bats at a wind energy facility in southern Alberta, Canada. Our goals were to better understand the influence of weather variables and turbine location on the activity and fatality of hoary bats (Lasiurus cinereus) and silver-haired bats (Lasionycteris noctivagans), and to use that understanding to predict variation in fatality rates at wind energy facilities and recommend measures to reduce fatalities. Overall activity of migratory bats and of silver-haired bats increased in low wind speeds and warm ambient temperatures, and was reduced when the wind was from the North or Northeast, whereas hoary bat activity increased with falling barometric pressure. Fatalities of migratory bats in general increased with increased activity of migratory bats, increased moon illumination, and falling barometric pressure and were influenced by the interaction between barometric pressure change and activity. Fatalities of silver-haired bats increased with increased activity, moon illumination, and winds from the south-east. Hoary bat fatalities increased with falling barometric pressure. Our results indicate that both the activity and fatality of migratory bats are affected by weather variables, but that species differ in their responses to environmental conditions. Spatially, fatalities were not influenced by the position of turbines within a turbine row, but were influenced by the location of turbines within the facility. Our findings have implications for our understanding of bat migration and efforts to reduce fatalities at wind energy facilities. To maximize the reduction of bat fatalities, operators of wind energy facilities could incorporate migratory bats' response to environmental variables, such as barometric pressure and fraction of moon illuminated, into their existing mitigation strategies. © 2011 The Wildlife Society.     

Predictive Occurrence Models for Bat Species in California

Abstract: The ability to accurately predict the potential occurrence of species of management concern is useful for wildlife managers, particularly for those whose management activities involve large areas where sampling is difficult due to logistical or financial constraints. During the summers of 2002 and 2003, we used mist nets to capture bats (Myotis yumanensis, M. californicus, M. evotis, M. thysanodes, Eptesicus fuscus, Lasionycteris noctivagans, Tadarida brasiliensis, Antrozous pallidus, Lasiurus borealis, and Lasiurus cinereus) in Whiskeytown National Recreation Area in north-central California, USA. We used landscape-scale variables, logistic regression, and Akaike's Information Criterion (AIC_c) to model species distributions and produce spatially discerning predictive occurrence maps. We developed a priori models that we used to determine which landscape-scale variables best discriminated between capture sites and non-capture sites. The odds of capturing a bat were 3.3 greater when total edge increased by 10,000 m, whereas for Yuma myotis (Myotis yumanensis), the odds of predicting presence were 0.2 greater when distance to lakes and ponds decreased by 2,000 m. Elevation was important in predicting the distribution of silver-haired bats (Lasionycteris noctivagans) and big brown bats (Eptesicus fuscus). Increasing elevation by 400 m decreased the odds of capturing a silver-haired bat by 0.1 and a big brown bat by 0.4. Classification accuracy for our models ranged from 80.9% for all bat species combined to 72.3% for Yuma myotis and silver-haired bats. Predictive occurrence models can be valuable to bat conservation efforts because they provide spatial data important for evaluating the effects of management activities on species distributions.     

Optimizing noninvasive sampling of a zoonotic bat virus

Outbreaks of infectious viruses resulting from spillover events from bats have brought much attention to bat‐borne zoonoses, which has motivated increased ecological and epidemiological studies on bat populations. Field sampling methods often collect pooled samples of bat excreta from plastic sheets placed under‐roosts. However, positive bias is introduced because multiple individuals may contribute to pooled samples, making studies of viral dynamics difficult. Here, we explore the general issue of bias in spatial sample pooling using Hendra virus in Australian bats as a case study. We assessed the accuracy of different under‐roost sampling designs using generalized additive models and field data from individually captured bats and pooled urine samples. We then used theoretical simulation models of bat density and under‐roost sampling to understand the mechanistic drivers of bias. The most commonly used sampling design estimated viral prevalence 3.2 times higher than individual‐level data, with positive bias 5–7 times higher than other designs due to spatial autocorrelation among sampling sheets and clustering of bats in roosts. Simulation results indicate using a stratified random design to collect 30–40 pooled urine samples from 80 to 100 sheets, each with an area of 0.75–1 m², and would allow estimation of true prevalence with minimum sampling bias and false negatives. These results show that widely used under‐roost sampling techniques are highly sensitive to viral presence, but lack specificity, providing limited information regarding viral dynamics. Improved estimation of true prevalence can be attained with minor changes to existing designs such as reducing sheet size, increasing sheet number, and spreading sheets out within the roost area. Our findings provide insight into how spatial sample pooling is vulnerable to bias for a wide range of systems in disease ecology, where optimal sampling design is influenced by pathogen prevalence, host population density, and patterns of aggregation.     

Hibernacula selection by Townsend's big-Eared bat in southwestern Colorado

In western United States, both mine reclamations and renewed mining at previously abandoned mines have increased substantially in the last decade. This increased activity may adversely impact bats that use these mines for roosting. Townsend's big-eared bat (Corynorhinus townsendii) is a species of conservation concern that may be impacted by ongoing mine reclamation and renewed mineral extraction. To help inform wildlife management decisions related to bat use of abandoned mine sites, we used logistic regression, Akaike's information criterion, and multi-model inference to investigate hibernacula use by Townsend's big-eared bats using 9 years of data from surveys inside abandoned mines in southwestern Colorado. Townsend's big-eared bats were found in 38 of 133 mines surveyed (29%), and occupied mines averaged 2.6 individuals per mine. The model explaining the most variability in our data included number of openings and portal temperature at abandoned mines. In southwestern Colorado, we found that abandoned mine sites with more than one opening and portal temperatures near 0°C were more likely to contain hibernating Townsend's big-eared bats. However, mines with only one opening and portal temperatures of ≥10°C were occasionally occupied by Townsend's big-eared bat. Understanding mine use by Townsend's big-eared bat can help guide decisions regarding allocation of resources and placement of bat-compatible closures at mine sites scheduled for reclamation. When feasible we believe that surveys should be conducted inside all abandoned mines in a reclamation project at least once during winter prior to making closure and reclamation recommendations. © 2010 The Wildlife Society.     

Habitat-associated and temporal patterns of bat activity in a diverse forest landscape of southern New England, USA

The development and use of acoustic recording technology, surveys have revealed the composition, relative levels of activity, and preliminary habitat use of bat communities of various forest locations. However, detailed examinations of acoustic surveys results to investigate temporal patterns of bat activity are rare. Initial active acoustic surveys of bat activity on the Quabbin Reservoir watershed in central Massachusetts recorded high numbers of call sequences by five bat species. The results showed the importance of aquatic and open habitats for bats in this diverse forest landscape. Because the preliminary surveys were restricted in extent (habitats), number (replicates), and duration (active surveys only, limited number of repeated surveys), the authors strongly recommended more comprehensive and extensive surveys of bat activity on the area. This paper reports the results of active (manual) and passive (automated) acoustic bat activity surveys in three replicates of ten habitats, conducted three to four times per year over the course of the study between 2004 and 2006. Over the full study, 2,800 actively-recorded 37,632 passively-recorded call sequences were examined and identified to species when possible. General patterns in activity did not differ by habitat between the two survey methods. Call sequences were recorded more often in open, especially aquatic habitats than in cluttered habitats. The use of passive surveys allowed for the assessment of temporal patterns of activity and an analysis of the relationship between ambient temperature and activity.     

Variance estimation for systematic designs in spatial surveys

Summary In spatial surveys for estimating the density of objects in a survey region, systematic designs will generally yield lower variance than random designs. However, estimating the systematic variance is well known to be a difficult problem. Existing methods tend to overestimate the variance, so although the variance is genuinely reduced, it is over‐reported, and the gain from the more efficient design is lost. The current approaches to estimating a systematic variance for spatial surveys are to approximate the systematic design by a random design, or approximate it by a stratified design. Previous work has shown that approximation by a random design can perform very poorly, while approximation by a stratified design is an improvement but can still be severely biased in some situations. We develop a new estimator based on modeling the encounter process over space. The new “striplet” estimator has negligible bias and excellent precision in a wide range of simulation scenarios, including strip‐sampling, distance‐sampling, and quadrat‐sampling surveys, and including populations that are highly trended or have strong aggregation of objects. We apply the new estimator to survey data for the spotted hyena (Crocuta crocuta) in the Serengeti National Park, Tanzania, and find that the reported coefficient of variation for estimated density is 20% using approximation by a random design, 17% using approximation by a stratified design, and 11% using the new striplet estimator. This large reduction in reported variance is verified by simulation.     

Mist Net Effort Required to Inventory a Forest Bat Species Assemblage

Abstract: Little quantitative information exists about the survey effort necessary to inventory temperate bat species assemblages. We used a bootstrap resampling algorithm to estimate the number of mist net surveys required to capture individuals from 9 species at both study area and site levels using data collected in a forested watershed in northwestern California, USA, during 1996–2000. The mean number of simulated surveys required to capture individual species varied with species' rarity and ranged from 1.5 to 44.9. We retrospectively evaluated strategies to reduce required survey effort by subsampling data from 1996 to 1998 and tested the strategies in the field during 1999 and 2000. Using data from 1996 to 1998, the mean number of simulated surveys required to capture 8 out of 9 species was 26.3, but a 95% probability of capture required >61 surveys. Inventory efficiency, defined as the cumulative proportion of species detected per survey effort, improved for both the study area and individual sites by conducting surveys later in summer. We realized further improvements in study area inventory efficiency by focusing on productive sites. We found that 3 surveys conducted between 1 July and 10 September at each of 4 productive sites in this 10-km² study area resulted in the capture of 8 species annually. Quantitative estimation of the survey effort required to assess bat species occurrence improves the ability to plan and execute reliable, efficient inventories. Results from our study should be useful for planning inventories in nearby geographical areas and similar habitat types; further, the analytical methods we used to assess effort are broadly applicable to other survey methods and taxa.     

Optimal design of butterfly occupancy surveys and testing if occupancy converts to abundance for sparse populations

Occupancy has several important advantages over abundance methods and may be the best choice for monitoring sparse populations. Here we use simulations to evaluate competing designs (number of sites vs. number of surveys) for occupancy monitoring, with emphasis on sparse populations of the endangered Karner blue butterfly (Lycaeides melissa samuelis Nabokov). Because conservation planning is usually abundance-based, we also ask whether detection/non-detection data may reliably convert to abundance, hypothesizing that occupancy provides a more dependable shortcut when populations are sparse. Count-index and distance sampling were conducted across 50 habitat patches containing variably sparse Karner blue populations. We used occupancy-detection model estimates as simulation inputs to evaluate primary replication tradeoffs, and used peak counts and population densities to evaluate the occupancy-abundance relationship. Detection probability and therefore optimal design of occupancy monitoring was strongly temperature dependent. Assuming a quality threshold of 0.075 root-mean square error for the occupancy estimator, the minimum allowable effort was 360 (40 sites?×?9 surveys) for spring generation and 200 (20 sites?×?10 surveys) for summer generation. A mixture model abundance estimator for repeated detection/non-detection data was biased low for high-density and low-density populations, suggesting that occupancy may not provide a reliable shortcut in abundance-based conservation planning for sparse butterfly populations.     

Catch Estimation with Restricted Randomization in the Effort Survey

One common method for estimating total catch is to multiply an estimate for CPUE, the catch per unit effort, by an estimate of total effort obtained from an independent second survey. In general, estimating total effort requires that sample times are chosen at random over the full fishing period; however, in practice, this may not always be possible and the usual estimator may be severely biased. Such a restriction in randomization is likely when aircraft are used to make instantaneous counts of fishing activity. This article proposes alternate estimators for use with both access and roving designs in conjunction with effort surveys for which sample times are not random. Ratio type estimators based on activity counts are developed under various scenarios and their performance examined under simulation. In addition, optimizing strategies for use with multiple activity counts are explored. Finally, data from an in-river gill net fishery on the Fraser River is used to illustrate these results.     

Potential of bat pass duration measures for studies of bat activity

Acoustic detectors have become increasingly used by bat workers to investigate bat ecology and assess the impacts of anthropogenic pressures. Within these studies, the metric used, ‘bat activity’, is based on the number of bat passes, without considering the bat pass duration (i.e. each event of a bat detected within the range of an ultrasonic detector). We expected that bat pass duration may contain information about site quality in terms of foraging potential. Because bats are expected to have a more sinuous trajectory and slower velocity when they exhibit foraging behaviour, as opposed to commuting behaviour, we hypothesize a longer bat pass duration in favourable habitats; during seasons with important energetic demands; or during night peak activity. We used datasets from a large-scale acoustic bat survey (n = 2890 sites), with a total of 24,597 bat pass measures from 6 taxa, and performed GLMM modelling. We detected a significant effect of habitat type on bat pass duration for five taxa. Shorter bat pass durations were detected at the beginning of the night. We detected longer pass durations during the lactation period or just before hibernating, while weather conditions or ageing and wear of the detector rarely influenced bat pass duration. Bat pass duration appears to be a simple and easy measure for position calls on a gradient between commuting vs. foraging behaviour. We suggest that the traditional measure of bat activity may be weighted by bat pass duration by giving more weight to events with potentially stronger links to foraging behaviour.