Multi-species monitoring of rare wetland fishes should account for imperfect detection of sampling devices

Accuracy in estimating occupancy of a threatened species is important for conservation but false absences bias many monitoring programs. Imperfect detection is especially relevant to surveys of rare wetland fishes which are often small-bodied and cryptic. Many factors influence probability of detection, including fish size and abundance, habitat characteristics and sampling devices. Imperfect detection can be addressed by accounting for probability of detection when estimating occupancy by modelling detection/non-detection data collected in replicate surveys. Three ecological specialists were once common in habitats associated with Lake Alexandrina at the terminus of the Murray–Darling Basin, Australia. The threatened Murray Hardyhead (Craterocephalus fluviatilis), Southern Pygmy Perch (Nannoperca australis) and Yarra Pygmy Perch (N. obscura) are now rare in the region following population collapses during a prolonged drought, and ongoing monitoring aims to assess their statuses for management purposes. This study compares probability of detection of the rare wetland fishes and cohabiting species during 2 years of multi-species monitoring using contrasting sampling devices (fyke and seine). The findings suggest large variations in estimated probability of detection can occur between devices for Murray Hardyhead and Southern Pygmy Perch. Yarra Pygmy Perch was undetected during the study. Overall, the findings show multi-species monitoring programs using a single sampling device may wrongly estimate the occupancy of a target fish. By accounting for imperfect detection, multi-species monitoring programs will improve inferences regarding population status, recovery and habitat quality of fishes to more accurately inform wetland management.     

Use of classical bird census transects as spatial replicates for hierarchical modeling of an avian community

New monitoring programs are often designed with some form of temporal replication to deal with imperfect detection by means of occupancy models. However, classical bird census data from earlier times often lack temporal replication, precluding detection‐corrected inferences about occupancy. Historical data have a key role in many ecological studies intended to document range shifts, and so need to be made comparable with present‐day data by accounting for detection probability. We analyze a classical bird census conducted in the region of Murcia (SE Spain) in 1991 and 1992 and propose a solution to estimating detection probability for such historical data when used in a community occupancy model: the spatial replication of subplots nested within larger plots allows estimation of detection probability. In our study, the basic sample units were 1‐km transects, which were considered spatial replicates in two aggregation schemes. We fit two Bayesian multispecies occupancy models, one for each aggregation scheme, and evaluated the linear and quadratic effect of forest cover and temperature, and a linear effect of precipitation on species occupancy probabilities. Using spatial rather than temporal replicates allowed us to obtain individual species occupancy probabilities and species richness accounting for imperfect detection. Species‐specific occupancy and community size decreased with increasing annual mean temperature. Both aggregation schemes yielded estimates of occupancy and detectability that were highly correlated for each species, so in the design of future surveys ecological reasons and cost‐effective sampling designs should be considered to select the most suitable aggregation scheme. In conclusion, the use of spatial replication may often allow historical survey data to be applied formally hierarchical occupancy models and be compared with modern‐day data of the species community to analyze global change process.     

Improving geographically extensive acoustic survey designs for modeling species occurrence with imperfect detection and misidentification

Acoustic recording units (ARUs) enable geographically extensive surveys of sensitive and elusive species. However, a hidden cost of using ARU data for modeling species occupancy is that prohibitive amounts of human verification may be required to correct species identifications made from automated software. Bat acoustic studies exemplify this challenge because large volumes of echolocation calls could be recorded and automatically classified to species. The standard occupancy model requires aggregating verified recordings to construct confirmed detection/non‐detection datasets. The multistep data processing workflow is not necessarily transparent nor consistent among studies. We share a workflow diagramming strategy that could provide coherency among practitioners. A false‐positive occupancy model is explored that accounts for misclassification errors and enables potential reduction in the number of confirmed detections. Simulations informed by real data were used to evaluate how much confirmation effort could be reduced without sacrificing site occupancy and detection error estimator bias and precision. We found even under a 50% reduction in total confirmation effort, estimator properties were reasonable for our assumed survey design, species‐specific parameter values, and desired precision. For transferability, a fully documented r package, OCacoustic, for implementing a false‐positive occupancy model is provided. Practitioners can apply OCacoustic to optimize their own study design (required sample sizes, number of visits, and confirmation scenarios) for properly implementing a false‐positive occupancy model with bat or other wildlife acoustic data. Additionally, our work highlights the importance of clearly defining research objectives and data processing strategies at the outset to align the study design with desired statistical inferences.     

Improving inferences from short‐term ecological studies with Bayesian hierarchical modeling: white‐headed woodpeckers in managed forests

Pilot studies are often used to design short‐term research projects and long‐term ecological monitoring programs, but data are sometimes discarded when they do not match the eventual survey design. Bayesian hierarchical modeling provides a convenient framework for integrating multiple data sources while explicitly separating sample variation into observation and ecological state processes. Such an approach can better estimate state uncertainty and improve inferences from short‐term studies in dynamic systems. We used a dynamic multistate occupancy model to estimate the probabilities of occurrence and nesting for white‐headed woodpeckers Picoides albolarvatus in recent harvest units within managed forests of northern California, USA. Our objectives were to examine how occupancy states and state transitions were related to forest management practices, and how the probabilities changed over time. Using Gibbs variable selection, we made inferences using multiple model structures and generated model‐averaged estimates. Probabilities of white‐headed woodpecker occurrence and nesting were high in 2009 and 2010, and the probability that nesting persisted at a site was positively related to the snag density in harvest units. Prior‐year nesting resulted in higher probabilities of subsequent occurrence and nesting. We demonstrate the benefit of forest management practices that increase the density of retained snags in harvest units for providing white‐headed woodpecker nesting habitat. While including an additional year of data from our pilot study did not drastically alter management recommendations, it changed the interpretation of the mechanism behind the observed dynamics. Bayesian hierarchical modeling has the potential to maximize the utility of studies based on small sample sizes while fully accounting for measurement error and both estimation and model uncertainty, thereby improving the ability of observational data to inform conservation and management strategies.     

Sampling bees in tropical forests and agroecosystems: a review

Bees are the predominant pollinating taxa, providing a critical ecosystem service upon which many angiosperms rely for successful reproduction. Available data suggests that bee populations worldwide are declining, but scarce data in tropical regions precludes assessing their status and distribution, impact on ecological services, and response to management actions. Herein, we reviewed >150 papers that used six common sampling methods (pan traps, baits, Malaise traps, sweep nets, timed observations and aspirators) to better understand their strengths and weaknesses, and help guide method selection to meet research objectives and development of multi-species monitoring approaches. Several studies evaluated the effectiveness of sweep nets, pan traps, and malaise traps, but only one evaluated timed observations, and none evaluated aspirators. Only five studies compared two or more of the remaining four sampling methods to each other. There was little consensus regarding which method would be most reliable for sampling multiple species. However, we recommend that if the objective of the study is to estimate abundance or species richness, malaise traps, pan traps and sweep nets are the most effective sampling protocols in open tropical systems; conversely, malaise traps, nets and baits may be the most effective in forests. Declining bee populations emphasize the critical need in method standardization and reporting precision. Moreover, we recommend reporting a catchability coefficient, a measure of the interaction between the resource (bee) abundance and catching effort. Melittologists could also consider existing methods, such as occupancy models, to quantify changes in distribution and abundance after modeling heterogeneity in trapping probability, and consider the possibility of developing monitoring frameworks that draw from multiple sources of data.     

Remote sensing of biodiversity: what to measure and monitor from space to species?

Global biodiversity monitoring systems through remote sensing can support consistent assessment, monitoring, modelling and reporting on biodiversity which are key activities intended for sustainable management. This work presents an overview of biodiversity monitoring components, i.e. biodiversity levels, essential biodiversity variables, biodiversity indicators, scale, biodiversity inventory, biodiversity models, habitat, ecosystem services, vegetation health and biogeochemical heterogeneity and discusses what remote sensing through Earth Observations has contributed to the study of biodiversity. The technological advancements in remote sensing have enabled information-rich data on biodiversity. Remote sensing data are making a strong contribution in providing unique information relevant to various biodiversity research and conservation applications. The extensive use of Earth observation data are not yet realized in biodiversity assessment, monitoring and conservation. The development of direct remote sensing approaches and the techniques for quantifying biodiversity at the community to species level is likely to be a great challenge for comprehensive earth observation-based monitoring strategy.

     

Assessing the conservation value of secondary savanna for large mammals in the Brazilian Cerrado

Debate about the conservation value of secondary habitats has tended to focus on tropical forests, increasingly recognizing the role of secondary forests for biodiversity conservation. However, there remains a lack of information about the conservation value of secondary savannas. Here, we conducted a camera trap survey to assess the effect of secondary vegetation on large mammals in a Brazilian Cerrado protected area, using a single‐season occupancy framework to investigate the response of individual species (species‐level models) and of all species combined (community‐level models). In addition, we investigated the cost effectiveness of different sampling designs to monitor globally threatened species in the study area. At the community level, savanna that regenerated from eucalyptus plantation had similar occupancy estimate as old growth areas. At the species level, none of the ten species individually assessed seemed to respond to succession stage, with greater support for the effect of other covariates on occupancy, such as distance from water and vegetation physiognomy. These results demonstrate that secondary vegetation does not appear to negatively impact large mammals in the study area and suggest that, given a favorable context, Cerrado mammals can recolonize and use secondary savannas that regenerated from clearcut. However, our study area should be considered a best‐case scenario, as it retained key ecological attributes of high‐value secondary habitats. Our simulations showed that a sampling design with 60 camera trap sites surveyed during nine occasions is appropriate to monitor most globally threatened species in the study area, and could be a useful starting point for new monitoring initiatives in other Cerrado areas.     

Evidence of region‐wide bat population decline from long‐term monitoring and Bayesian occupancy models with empirically informed priors

Strategic conservation efforts for cryptic species, especially bats, are hindered by limited understanding of distribution and population trends. Integrating long‐term encounter surveys with multi‐season occupancy models provides a solution whereby inferences about changing occupancy probabilities and latent changes in abundance can be supported. When harnessed to a Bayesian inferential paradigm, this modeling framework offers flexibility for conservation programs that need to update prior model‐based understanding about at‐risk species with new data. This scenario is exemplified by a bat monitoring program in the Pacific Northwestern United States in which results from 8 years of surveys from 2003 to 2010 require updating with new data from 2016 to 2018. The new data were collected after the arrival of bat white‐nose syndrome and expansion of wind power generation, stressors expected to cause population declines in at least two vulnerable species, little brown bat (Myotis lucifugus) and the hoary bat (Lasiurus cinereus). We used multi‐season occupancy models with empirically informed prior distributions drawn from previous occupancy results (2003–2010) to assess evidence of contemporary decline in these two species. Empirically informed priors provided the bridge across the two monitoring periods and increased precision of parameter posterior distributions, but did not alter inferences relative to use of vague priors. We found evidence of region‐wide summertime decline for the hoary bat ( = 0.86 ± 0.10) since 2010, but no evidence of decline for the little brown bat ( = 1.1 ± 0.10). White‐nose syndrome was documented in the region in 2016 and may not yet have caused regional impact to the little brown bat. However, our discovery of hoary bat decline is consistent with the hypothesis that the longer duration and greater geographic extent of the wind energy stressor (collision and barotrauma) have impacted the species. These hypotheses can be evaluated and updated over time within our framework of pre–post impact monitoring and modeling. Our approach provides the foundation for a strategic evidence‐based conservation system and contributes to a growing preponderance of evidence from multiple lines of inquiry that bat species are declining.     

Context dependency in interference competition among birds in an endangered woodland ecosystem

Aim

Much research has quantified species responses to human-modified ecosystems. However, there is limited work on how human-modified ecosystems may reshape competitive interactions between species. Using a 19-year study across 3 million ha, we aimed to answer the question: Are levels of interference competition between bird species context dependent and influenced by habitat structure and productivity? We focussed on the hyper-aggressive behaviour of the Noisy Miner (Manorina melanocephala), which is recognized as a key threatening process for other woodland bird species in Australia. Whether environmental conditions such as amount of forest cover and net primary productivity (NPP) mediate the Noisy Miners' impact remains untested at large spatiotemporal scales.

Location

Temperate woodlands of south-eastern Australia.

Methods

We gathered data on bird site occupancy from repeated surveys of field sites and assembled satellite data on tree cover and NPP. We constructed Bayesian multi-species occupancy/detection models of bird species in woodland patches and tested the fixed and interactive effects of Noisy Miner presence, the amount of tree cover, NPP, and time. We quantified the responses of 31 species, many with known interactions with the Noisy Miner documented previously at fine spatial scales.

Results

We identified negative associations between the Noisy Miner and 18 bird species, including, unexpectedly, both small and large bodied taxa. Site occupancy in some species was influenced by interactions between Noisy Miner presence and increasing amounts of tree cover or productivity. For some species, interference competition by the Noisy Miner is context-dependent and mitigated by increasing tree cover and/or increasing NPP.

Main Conclusions

Our analyses revealed that woodland bird conservation in our study region will be promoted by protecting refugia characterized by areas of high NPP and high tree cover. Preventing vegetation clearing that reduces tree cover could reduce interference competition by the Noisy Miner on parts of the remaining woodland bird community, including species of conservation concern.     

The importance of incorporating imperfect detection in biodiversity assessments: a case study of small mammals in an Australian region

Aim Assessments of biodiversity are an essential requirement of conservation management planning. Species distributional modelling is a popular approach to quantifying biodiversity whereby occurrence data are related to environmental covariates. An important confounding factor that is often overlooked in the development of such models is uncertainty due to imperfect detection. Here, I demonstrate how an analytical approach that accounts for the bias due to imperfect detection can be applied retrospectively to an existing biodiversity survey data set to produce more realistic estimates of species distributions and unbiased covariate relationships. Location Pilbara biogeographic region, Australia. Methods As a component of the Pilbara survey, presence/absence (i.e. undetected) data on small ground‐dwelling mammals were collected. I applied a multiseason occupancy modelling approach to six of the most common species encountered during this survey. Detection and occupancy rates, as well as extinction and colonization probabilities, were determined, and the influence of covariates on these parameters was examined using the multi‐model inference approach. Results Detection probabilities for all six species were considerably lower than 1.0 and varied across time and species. Naïve estimates of occupancy underestimated occupancy rates corrected for species detectability by up to 45%. Seasonal variation in occupancy status was attributed to changes in detection for two of the focal species, while reproductive events explained variation in occupancy in three others. Covariates describing the substrate strongly influenced site occupancy for most of the species modelled. A comparison of the occupancy model with a generalized linear model, assuming perfect detection, showed that the effects of the covariates were underestimated in the latter model. Main conclusions The application of the multiseason occupancy modelling approach to the Pilbara mammal data set demonstrated a powerful framework for examining changes in site occupancy, as well as local colonization and extinction rates of species which are not confounded by variable species detection rates.     

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.     

Standardized Assessment of Biodiversity Trends in Tropical Forest Protected Areas: The End Is Not in Sight

Extinction rates in the Anthropocene are three orders of magnitude higher than background and disproportionately occur in the tropics, home of half the world’s species. Despite global efforts to combat tropical species extinctions, lack of high-quality, objective information on tropical biodiversity has hampered quantitative evaluation of conservation strategies. In particular, the scarcity of population-level monitoring in tropical forests has stymied assessment of biodiversity outcomes, such as the status and trends of animal populations in protected areas. Here, we evaluate occupancy trends for 511 populations of terrestrial mammals and birds, representing 244 species from 15 tropical forest protected areas on three continents. For the first time to our knowledge, we use annual surveys from tropical forests worldwide that employ a standardized camera trapping protocol, and we compute data analytics that correct for imperfect detection. We found that occupancy declined in 22%, increased in 17%, and exhibited no change in 22% of populations during the last 3–8 years, while 39% of populations were detected too infrequently to assess occupancy changes. Despite extensive variability in occupancy trends, these 15 tropical protected areas have not exhibited systematic declines in biodiversity (i.e., occupancy, richness, or evenness) at the community level. Our results differ from reports of widespread biodiversity declines based on aggregated secondary data and expert opinion and suggest less extreme deterioration in tropical forest protected areas. We simultaneously fill an important conservation data gap and demonstrate the value of large-scale monitoring infrastructure and powerful analytics, which can be scaled to incorporate additional sites, ecosystems, and monitoring methods. In an era of catastrophic biodiversity loss, robust indicators produced from standardized monitoring infrastructure are critical to accurately assess population outcomes and identify conservation strategies that can avert biodiversity collapse.     

A case for multi-species management of sympatric herbivore pest impacts in the central Southern Alps, New Zealand

Five herbivorous introduced mammals are sympatric in the central Southern Alps. All of these species have the potential to affect conservation values, yet the Department of Conservation at present monitors and mitigates the impacts of only one. We outline ecological arguments for multi-species management of sympatric herbivore pest impacts and use the two- species system of sympatric thar and chamois to highlight the need for multi-species management of the central Southern Alps alpine pest community.     

Testing Three Species Distribution Modelling Strategies to Define Fish Assemblage Reference Conditions for Stream Bioassessment and Related Applications

Species distribution models are widely used for stream bioassessment, estimating changes in habitat suitability and identifying conservation priorities. We tested the accuracy of three modelling strategies (single species ensemble, multi-species response and community classification models) to predict fish assemblages at reference stream segments in coastal subtropical Australia. We aimed to evaluate each modelling strategy for consistency of predictor variable selection; determine which strategy is most suitable for stream bioassessment using fish indicators; and appraise which strategies best match other stream management applications. Five models, one single species ensemble, two multi-species response and two community classification models, were calibrated using fish species presence-absence data from 103 reference sites. Models were evaluated for generality and transferability through space and time using four external reference site datasets. Elevation and catchment slope were consistently identified as key correlates of fish assemblage composition among models. The community classification models had high omission error rates and contributed fewer taxa to the ‘expected’ component of the taxonomic completeness (O/E₅₀) index than the other strategies. This potentially decreases the model sensitivity for site impact assessment. The ensemble model accurately and precisely modelled O/E₅₀ for the training data, but produced biased predictions for the external datasets. The multi-species response models afforded relatively high accuracy and precision coupled with low bias across external datasets and had lower taxa omission rates than the community classification models. They inherently included rare, but predictable species while excluding species that were poorly modelled among all strategies. We suggest that the multi-species response modelling strategy is most suited to bioassessment using freshwater fish assemblages in our study area. At the species level, the ensemble model exhibited high sensitivity without reductions in specificity, relative to the other models. We suggest that this strategy is well suited to other non-bioassessment stream management applications, e.g., identifying priority areas for species conservation.     

Estimating occupancy dynamics for large‐scale monitoring networks: amphibian breeding occupancy across protected areas in the northeast United States

Regional monitoring strategies frequently employ a nested sampling design where a finite set of study areas from throughout a region are selected and intensive sampling occurs within a subset of sites within the individual study areas. This sampling protocol naturally lends itself to a hierarchical analysis to account for dependence among subsamples. Implementing such an analysis using a classic likelihood framework is computationally challenging when accounting for detection errors in species occurrence models. Bayesian methods offer an alternative approach for fitting models that readily allows for spatial structure to be incorporated. We demonstrate a general approach for estimating occupancy when data come from a nested sampling design. We analyzed data from a regional monitoring program of wood frogs (Lithobates sylvaticus) and spotted salamanders (Ambystoma maculatum) in vernal pools using static and dynamic occupancy models. We analyzed observations from 2004 to 2013 that were collected within 14 protected areas located throughout the northeast United States. We use the data set to estimate trends in occupancy at both the regional and individual protected area levels. We show that occupancy at the regional level was relatively stable for both species. However, substantial variation occurred among study areas, with some populations declining and some increasing for both species. In addition, When the hierarchical study design is not accounted for, one would conclude stronger support for latitudinal gradient in trends than when using our approach that accounts for the nested design. In contrast to the model that does not account for nesting, the nested model did not include an effect of latitude in the 95% credible interval. These results shed light on the range‐level population status of these pond‐breeding amphibians, and our approach provides a framework that can be used to examine drivers of local and regional occurrence dynamics.     

Determining Occurrence Dynamics when False Positives Occur: Estimating the Range Dynamics of Wolves from Public Survey Data

Large-scale presence-absence monitoring programs have great promise for many conservation applications. Their value can be limited by potential incorrect inferences owing to observational errors, especially when data are collected by the public. To combat this, previous analytical methods have focused on addressing non-detection from public survey data. Misclassification errors have received less attention but are also likely to be a common component of public surveys, as well as many other data types. We derive estimators for dynamic occupancy parameters (extinction and colonization), focusing on the case where certainty can be assumed for a subset of detections. We demonstrate how to simultaneously account for non-detection (false negatives) and misclassification (false positives) when estimating occurrence parameters for gray wolves in northern Montana from 2007–2010. Our primary data source for the analysis was observations by deer and elk hunters, reported as part of the state’s annual hunter survey. This data was supplemented with data from known locations of radio-collared wolves. We found that occupancy was relatively stable during the years of the study and wolves were largely restricted to the highest quality habitats in the study area. Transitions in the occupancy status of sites were rare, as occupied sites almost always remained occupied and unoccupied sites remained unoccupied. Failing to account for false positives led to over estimation of both the area inhabited by wolves and the frequency of turnover. The ability to properly account for both false negatives and false positives is an important step to improve inferences for conservation from large-scale public surveys. The approach we propose will improve our understanding of the status of wolf populations and is relevant to many other data types where false positives are a component of observations.     

Development of multi-species indicators for the Nevada Wildlife Action Plan

Wildlife managers, conservationists, and policy-makers are interested in indicators that aggregate population status and/or trend information for multiple species. Several multi-species indicators have been proposed, which either report species status ranks or rank changes (using either the International Union for the Conservation of Nature or NatureServe ranking systems), or which report the average direction or tendency of time series from multiple populations or species. We explore the utility of these indicators within the context of the Nevada Wildlife Action Plan, one of 56 state or territorial wildlife conservation plans that have recently been developed and published by state wildlife agencies in the USA. The Nevada Wildlife Action Plan identifies 186 “species of greatest conservation need,” including birds, mammals, reptiles, amphibians, fish, and mollusks. Significant deficiencies in the existing monitoring data for these taxa limit the applicability of nearly all of the multi-species indicators that have been described to date. However, we were able to populate a simple multi-species status indicator with data for all 186 species. We also describe a simple graphical indicator that summarizes information about the direction of population trends for suites of multiple species, and discuss how this tool could be populated with data in future revisions of the Nevada Wildlife Action Plan.     

Local habitat measures derived from aerial pictures are not strong predictors of amphibian occurrence or abundance

Species monitoring plays an important role in determining whether conservation targets are being met. However, monitoring programs can be costly and logistically demanding. When site characteristics are strongly linked to species’ status, managers may instead choose to monitor the site characteristics themselves as a surrogate of species status. In this study, we modelled the occupancy status and abundance of pond-breeding amphibians in a network of protected areas across Switzerland. We incorporated remotely-sensed data describing habitat within breeding sites in order to identify any characteristics which could act as monitoring surrogates for amphibian species’ status. We found that connectivity between amphibian breeding sites was an important predictor of occupancy patterns for all species, but that abundance patterns were poorly predicted. Despite expectations that the habitat characteristics assessed from aerial images were important for the species studied, we found that these variables were rarely strong predictors of occupancy patterns. These results highlight the importance of caution in identifying species monitoring surrogates, and the need to explicitly demonstrate strong relationships between surrogates and state variables of interest before surrogates are used.