Inferring red squirrel (<Emphasis Type="Italic">Sciurus vulgaris</Emphasis>) absence with hair tubes surveys: a sampling protocol

Hair tubes are often used to monitor red squirrel in fragmented landscapes, where presence/absence data are gathered to determine its distribution and factors affecting it. Despite many applications and evaluation of this technique for density estimation, the reliability of absence data has been overlooked, as no rigorous statistical estimate has been attempted both on the survey duration and on the reliability of absences. Accurate determination of the duration of a survey (e.g. how many visits should be carried out to consider the species absent rather than non-detected) will affect total costs and number of monitored sites; moreover, false absences will bias the distribution estimates. By applying some recently developed occupancy models, we estimated detection probability and sampling size required to infer red squirrel absence. Application of this sampling and data analysis protocol allows to infer the species absence at a reasonable cost and thus to evaluate the reliability of a presence/absence dataset.     

Optimal eradication: when to stop looking for an invasive plant

The notion of being sure that you have completely eradicated an invasive species is fanciful because of imperfect detection and persistent seed banks. Eradication is commonly declared either on an ad hoc basis, on notions of seed bank longevity, or on setting arbitrary thresholds of 1% or 5% confidence that the species is not present. Rather than declaring eradication at some arbitrary level of confidence, we take an economic approach in which we stop looking when the expected costs outweigh the expected benefits. We develop theory that determines the number of years of absent surveys required to minimize the net expected cost. Given detection of a species is imperfect, the optimal stopping time is a trade-off between the cost of continued surveying and the cost of escape and damage if eradication is declared too soon. A simple rule of thumb compares well to the exact optimal solution using stochastic dynamic programming. Application of the approach to the eradication programme of Helenium amarum reveals that the actual stopping time was a precautionary one given the ranges for each parameter.     

Integrating over uncertainty in spatial scale of response within multispecies occupancy models yields more accurate assessments of community composition

Species abundance and community composition are affected not only by the local environment, but also by broader landscape and regional context. Yet, determining the spatial scales at which landscapes affect species remains a persistent challenge, hindering our ability to understand how environmental gradients shape communities. This problem is amplified by rare species and imperfect species detection. Here, we present a Bayesian framework that allows uncertainty surrounding the ‘true’ spatial scale of species’ responses (i.e. changes in presence/absence) to be integrated directly into a community hierarchical model. This scale‐selecting multispecies occupancy model (ssMSOM) estimates the scale of response, and shows high accuracy and correct levels of uncertainty in parameter estimates across a broad range of simulation conditions. An ssMSOM can be run in a matter of minutes, as opposed to the many hours required to run normal multispecies occupancy models at all queried spatial scales, and then conduct model selection – a problem that up to now has prohibited scale of response from being rigorously evaluated in an occupancy framework. Alternatives to the ssMSOM, such as GLM‐based approaches frequently fail to detect the correct spatial scale and magnitude of response, and are often falsely confident by favoring the incorrect parameter estimates, especially as species’ detection probabilities deviate from perfect. We further show how trait information can be leveraged to understand how individual species’ scales of response vary within communities. Integrating spatial scale selection directly into hierarchical community models provides a means of formally testing hypotheses regarding spatial scales of response, and more accurately determining the environmental drivers that shape communities.     

The morphology of the imperfect states of powdery mildews (Erysiphaceae)

In contrast to popular belief, a rich variety of morphological characteristics exists in the imperfect states of powdery mildews. Because it has been generally assumed that species cannot be distinguished by their appressoria, haustoria, conidiophores, conidia, fibrosin bodies, and conidial germ tubes, their morphology has received little attention and several older publications have even been forgotten. As with the perfect states, few species can be recognized by one characteristic of the imperfect state alone but many species can be identified when a combination of several characteristics is used. Important characteristics are the location of the mycelium, the production of conidia singly or in chains, the presence or absence of conspicuous fibrosin bodies, the appressoria, the size and shape of the conidia, and the position and type of their germ tubes. Many species are associated with particular families or genera of plants and therefore these are included in a key to identify 131 species of powdery mildew. This key shows how much and especially how little is known about many species. It is hoped that this review will stimulate study of the morphology of the imperfect states of numerous species. Consideration of both the perfect and the imperfect state should result in a more natural classification of several genera, for exampleUncinula andErysiphe which at present include both species which produce conidia in long chains and those which produce conidia singly. It appears that there are two lines of development of the imperfect states. One is characterized by lobed appressoria and conidiophores which produce conidia singly. The other is characterized by more or less rounded, unlobed appressoria and conidiophores which produce conidia in chains. A better knowledge of all the different imperfect states may provide more information regarding the evolution of powdery mildews.     

Spatially explicit estimation of occupancy,detection probability and survey effort needed to inform conservation planning

Aim It is increasingly recognized the importance of accounting for imperfect detection in species distribution modelling and conservation planning. However, the integration of detectability into a spatially explicit frame has received little attention. We aim (1) to show how to develop distribution maps of both detection probability and survey effort required to reliably determine a species presence/absence and (2) to increase awareness of the spatial variation of detection error inherent in studies of species occurrence. Location North‐western Spain. Methods  We registered the presence/absence of the endangered Egyptian vulture (Neophron percnopterus) in 213 surveys performed in 40 of 104 territories once known to be occupied. We model simultaneously both detection probability and occurrence, using site occupancy modelling. With the resulting regression equations, we developed distribution maps of both detection probability and required sampling effort throughout the area. Results Of the studied territories, 72.5% were detected as occupied, but after accounting for imperfect detection, the proportion of sites truly occupied was 79%. Detectability decreased in territories with higher topographical irregularity and increased with both the time of day of the survey and the progress of the season. Spatial distribution of detectability showed a mainly north–south gradient following the distribution of slope in the area. The likelihood of occupancy increased with rockier, less forested surface and less topographical irregularity within the territory. A minimum of five surveys, on average, are needed to assess, with 95% probability, the occupancy status of a site, ranging from ≤ 3 to > 24 visits/territory depending on survey‐ and site‐specific features. Main conclusions Accounting for detectability and its sources of variation allows us to elaborate distribution maps of detectability‐based survey effort. These maps are useful tools to reliably assess (e.g. with 95% probability) occupancy status throughout a landscape and provide guidance for species conservation planning.     

Site-occupancy models may offer new opportunities for dragonfly monitoring based on daily species lists

Monitoring biodiversity is necessary but difficult to achieve in practice, in part because standardized field work is often demanding for volunteer field workers. Collecting opportunistic data on presence and absence of species is much less demanding, but such data may suffer from a number of biases, such as variation in observation effort over time. Here we explore whether site-occupancy models may be helpful to reduce such biases in opportunistic data, especially those caused by temporal variation of observation effort and by incomplete reporting of sightings. Site-occupancy models represent a generalisation of classical metapopulation models to account for imperfect detection; they estimate the probability of sites to be occupied (and of the rates of change, colonisation and extinction rates) while taking into account imperfect detection of a species. The models require so-called presence–absence data from replicated visits for a number of sites (e.g., 20–50). We tested whether these models provide reliable trend estimates if collectors of opportunistic data do not report all species detected. We applied the models to three opportunistic datasets of dragonfly species (1999–2007) in the Netherlands: (1) one-species records, (2) short daily species lists and (3) comprehensive daily species lists. Trend estimates based on a fourth dataset from a standardized monitoring scheme were used as a yardstick to judge the results.The analyses showed that occupancy trends based on comprehensive daily species lists in combination with site-occupancy models were generally similar to those based on the monitoring scheme. But trends based on one-species records and short daily lists were too imprecise to be very useful. In addition, site-occupancy models lead to more realistic occupancy estimates than those obtained from conventional logistic regression analysis. We conclude that comprehensive daily species lists can be useful surrogates for monitoring schemes to assess distributional trends.     

Introduction to the symposium on search and detection

[First paragraph...] Those managing biosecurity at borders or eradicating weeds, pests and diseases share a common problem. Unless the unwanted organism is highly conspicuous, it can be extremely difficult to find the first few invaders, or the last few survivors. Thus, non- detection after a search does not necessarily mean the animal, plant or disease is not in fact present. Depending on the effectiveness of the search, the absence of evidence may provide only weak evidence of absence. These uncertainties create risks for managers. Falsely declaring an unwanted organism absent when it is in fact present but undetected is likely to have adverse, and potentially major, biological, economic and political consequences. Conversely, it is obviously wasteful to continue with management and surveillance when in truth the organism is no longer present.     

The Optimal Number of Surveys when Detectability Varies

The survey of plant and animal populations is central to undertaking field ecology. However, detection is imperfect, so the absence of a species cannot be determined with certainty. Methods developed to account for imperfect detectability during surveys do not yet account for stochastic variation in detectability over time or space. When each survey entails a fixed cost that is not spent searching (e.g., time required to travel to the site), stochastic detection rates result in a trade-off between the number of surveys and the length of each survey when surveying a single site. We present a model that addresses this trade-off and use it to determine the number of surveys that: 1) maximizes the expected probability of detection over the entire survey period; and 2) is most likely to achieve a minimally-acceptable probability of detection. We illustrate the applicability of our approach using three practical examples (minimum survey effort protocols, number of frog surveys per season, and number of quadrats per site to detect a plant species) and test our model''s predictions using data from experimental plant surveys. We find that when maximizing the expected probability of detection, the optimal survey design is most sensitive to the coefficient of variation in the rate of detection and the ratio of the search budget to the travel cost. When maximizing the likelihood of achieving a particular probability of detection, the optimal survey design is most sensitive to the required probability of detection, the expected number of detections if the budget were spent only on searching, and the expected number of detections that are missed due to travel costs. We find that accounting for stochasticity in detection rates is likely to be particularly important for designing surveys when detection rates are low. Our model provides a framework to do this.     

Assessing the impacts of imperfect detection on estimates of diversity and community structure through multispecies occupancy modeling

Detecting all species in a given survey is challenging, regardless of sampling effort. This issue, more commonly known as imperfect detection, can have negative impacts on data quality and interpretation, most notably leading to false absences for rare or difficult‐to‐detect species. It is important that this issue be addressed, as estimates of species richness are critical to many areas of ecological research and management. In this study, we set out to determine the impacts of imperfect detection, and decisions about thresholds for inclusion in occupancy, on estimates of species richness and community structure. We collected data from a stream fish assemblage in Algonquin Provincial Park to be used as a representation of ecological communities. We then used multispecies occupancy modeling to estimate species‐specific occurrence probabilities while accounting for imperfect detection, thus creating a more informed dataset. This dataset was then compared to the original to see where differences occurred. In our analyses, we demonstrated that imperfect detection can lead to large changes in estimates of species richness at the site level and summarized differences in the community structure and sampling locations, represented through correspondence analyses.     

Population size influences amphibian detection probability: implications for biodiversity monitoring programs

Monitoring is an integral part of species conservation. Monitoring programs must take imperfect detection of species into account in order to be reliable. Theory suggests that detection probability may be determined by population size but this relationship has not yet been assessed empirically. Population size is particularly important because it may induce heterogeneity in detection probability and thereby cause bias in estimates of biodiversity. We used a site occupancy model to analyse data from a volunteer-based amphibian monitoring program to assess how well different variables explain variation in detection probability. An index to population size best explained detection probabilities for four out of six species (to avoid circular reasoning, we used the count of individuals at a previous site visit as an index to current population size). The relationship between the population index and detection probability was positive. Commonly used weather variables best explained detection probabilities for two out of six species. Estimates of site occupancy probabilities differed depending on whether the population index was or was not used to model detection probability. The relationship between the population index and detectability has implications for the design of monitoring and species conservation. Most importantly, because many small populations are likely to be overlooked, monitoring programs should be designed in such a way that small populations are not overlooked. The results also imply that methods cannot be standardized in such a way that detection probabilities are constant. As we have shown here, one can easily account for variation in population size in the analysis of data from long-term monitoring programs by using counts of individuals from surveys at the same site in previous years. Accounting for variation in population size is important because it can affect the results of long-term monitoring programs and ultimately the conservation of imperiled species.     

Designing occupancy surveys and interpreting non‐detection when observations are imperfect

Aim Conservation practitioners use biological surveys to ascertain whether or not a site is occupied by a particular species. Widely used statistical methods estimate the probability that a species will be detected in a survey of an occupied site. However, these estimates of detection probability are alone not sufficient to calculate the probability that a species is present given that it was not detected. The aim of this paper is to demonstrate methods for correctly calculating (1) the probability a species occupies a site given one or more non‐detections, and (2) the number of sequential non‐detections necessary to assert, with a pre‐specified confidence, that a species is absent from a site. Location Occupancy data for a tree frog in eastern Australia serve to illustrate methods that may be applied anywhere species’ occupancy data are used and detection probabilities are < 1. Methods Building on Bayesian expressions for the probability that a site is occupied by a species when it is not detected, and the number of non‐detections necessary to assert absence with a pre‐specified confidence, we estimate occupancy probabilities across tree frog survey locations, drawing on information about where and when the species was detected during surveys. Results We show that the number of sequential non‐detections necessary to assert that a species is absent increases nonlinearly with the prior probability of occupancy, the probability of detection if present, and the desired level of confidence about absence. Main conclusions If used more widely, the Bayesian analytical approaches illustrated here would improve collection and interpretation of biological survey data, providing a coherent way to incorporate detection probability estimates in the design of minimum survey requirements for monitoring, impact assessment and distribution modelling.     

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.     

Spatial patch occupancy patterns of the Lower Keys marsh rabbit

Reliable estimates of presence or absence of a species can provide substantial information on management questions related to distribution and habitat use but should incorporate the probability of detection to reduce bias. We surveyed for the endangered Lower Keys marsh rabbit (Sylvilagus palustris hefneri) in habitat patches on 5 Florida Key islands, USA, to estimate occupancy and detection probabilities. We derived detection probabilities using spatial replication of plots and evaluated hypotheses that patch location (coastal or interior) and patch size influence occupancy and detection. Results demonstrate that detection probability, given rabbits were present, was <0.5 and suggest that naïve estimates (i.e., estimates without consideration of imperfect detection) of patch occupancy are negatively biased. We found that patch size and location influenced probability of occupancy but not detection. Our findings will be used by Refuge managers to evaluate population trends of Lower Keys marsh rabbits from historical data and to guide management decisions for species recovery. The sampling and analytical methods we used may be useful for researchers and managers of other endangered lagomorphs and cryptic or fossorial animals occupying diverse habitats. © 2011 The Wildlife Society.     

Development and application of environmental DNA surveillance for the threatened crucian carp (Carassius carassius)

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Can we model the probability of presence of species without absence data?

In ecological studies, it is useful to estimate the probability that a species occurs at given locations. The probability of presence can be modeled by traditional statistical methods, if both presence and absence data are available. However, the challenge is that most species records contain only presence data, without reliable absence data. Previous presence‐only methods can estimate a relative index of habitat suitability, but cannot estimate the actual probability of presence. In this study, we develop a presence and background learning algorithm (PBL) that is successful in modeling the conditional probability of presence of a simulated species. The model is trained by two completely separate sets: observed presence and background data. Assuming that the probability of presence is one for ‘prototypical presence’ locations where the habitats are maximally suitable for a species, we can estimate a constant that can calibrate the trained model into the actual probability of presence. Experimental results show that the PBL method performs similarly to a presence‐absence method, and significantly better than the widely used maximum entropy method. The new algorithm enables us to model the probability that a species occurs conditional on environmental covariates without absence data. Hence, it has potential to improve modeling of the geographical distributions of species.     

The importance of including imperfect detection models in eDNA experimental design

Environmental DNA (eDNA) is DNA that has been isolated from field samples, and it is increasingly used to infer the presence or absence of particular species in an ecosystem. However, the combination of sampling procedures and subsequent molecular amplification of eDNA can lead to spurious results. As such, it is imperative that eDNA studies include a statistical framework for interpreting eDNA presence/absence data. We reviewed published literature for studies that utilized eDNA where the species density was known and compared the probability of detecting the focal species to the sampling and analysis protocols. Although biomass of the target species and the volume per sample did not impact detectability, the number of field replicates and number of samples from each replicate were positively related to detection. Additionally, increased number of PCR replicates and increased primer specificity significantly increased detectability. Accordingly, we advocate for increased use of occupancy modelling as a method to incorporate effects of sampling effort and PCR sensitivity in eDNA study design. Based on simulation results and the hierarchical nature of occupancy models, we suggest that field replicates, as opposed to molecular replicates, result in better detection probabilities of target species.     

Investigating detection success: lessons from trials using decoy rare plants

Imperfect detection leads to underestimates of species presence and decreases the reliability of survey data. Imperfect detection has not been examined in detail for boreal forest understory plants, despite widespread use of surveys for rare plants prior to development. We addressed this issue using detectability trials conducted in Alberta, Canada with decoy vascular plants. Volunteer observers searched in survey plots for species while unaware of their true presence or abundance. Our findings indicate that the detection of cryptic species is very low when abundance is low (0–35%) and plot size is large (<?50% in?≥?100 m²). Plant density (individuals per unit area) was the most important determinant of detection probability, where more abundant species were detected more often and with less survey effort. When abundance was held constant, diffusely arranged species were twice as likely to be detected compared to those in clumps. Detection of cryptic species can be low even when individuals are flowering, and even morphologically distinct species can go unnoticed in small plots. We suggest that future decoy trials investigate search strategies that could improve detection and that field surveys for vascular plants address imperfect detection through careful consideration of plot size, characteristics of the target species, and survey effort, both in terms of time expenditure within an area and the number of observers employed.     

Initiation of deoxyribonucleic acid replication in a temperature-sensitive mutant of B. subtilis: evidence for a transcriptional step

A mutant of Bacillus subtilis unable to initiate a new round of replication at 45 C has been described. Here we show that inhibition of DNA synthesis in this mutant is reversible and that DNA synthesis is resumed at low temperature, even in the presence of chloramphenicol. Initiation of a new replication cycle thus can occur in the absence of protein synthesis. A thermolabile component required for initiation therefore appears to be synthesized at 45 C in an inactive form and can be activated at 30 C in the presence of an inhibitor of protein synthesis. Although resistant to chloramphenicol, the reinitiation of replication occurring after lowering the temperature is sensitive to rifampin and streptolydigin.     

Dependence of Clostridium botulinum gas and protease production on culture conditions

Reports that Clostridium botulinum toxin can sometimes be detected in the absence of indicators of overt spoilage led to a systematic study of this phenomenon in a model system. Media with various combinations of pH (5.0 to 7.0) and glucose (0.0 to 1.0%) were inoculated with vegetative cells of C. botulinum 62A and incubated anaerobically at 35 degrees C. Although growth and toxin production occurred at all pH and glucose combinations, accumulation of gas was delayed or absent in media with low pH, low glucose levels, or both. Other proteolytic C. botulinum strains gave similar results. Trypsin activation was required to detect toxin in some low pH cultures. The trypsinization requirement correlated with low proteolytic activity in the cultures. Proteolytic activity of the strains examined was 5- to 500-fold lower in botulinal assay medium than in cooked meat medium. The results indicate that the absence of gas accumulation does not preclude the presence of botulinal toxin and that proteolytic cultures grown under adverse conditions may require trypsinization for the detection of toxin.     

Dependence of Clostridium botulinum gas and protease production on culture conditions.

Reports that Clostridium botulinum toxin can sometimes be detected in the absence of indicators of overt spoilage led to a systematic study of this phenomenon in a model system. Media with various combinations of pH (5.0 to 7.0) and glucose (0.0 to 1.0%) were inoculated with vegetative cells of C. botulinum 62A and incubated anaerobically at 35 degrees C. Although growth and toxin production occurred at all pH and glucose combinations, accumulation of gas was delayed or absent in media with low pH, low glucose levels, or both. Other proteolytic C. botulinum strains gave similar results. Trypsin activation was required to detect toxin in some low pH cultures. The trypsinization requirement correlated with low proteolytic activity in the cultures. Proteolytic activity of the strains examined was 5- to 500-fold lower in botulinal assay medium than in cooked meat medium. The results indicate that the absence of gas accumulation does not preclude the presence of botulinal toxin and that proteolytic cultures grown under adverse conditions may require trypsinization for the detection of toxin.