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18 results for “imperfect detection”
Investigating cooccurrence patterns and dynamics for many imperfectly detected species, using a log-linear modelling parameterisation
<p>1. Patterns in, and the underlying dynamics of, species cooccurrence is of interest in many ecological applications. Unaccounted for, imperfect detection of the species can lead to misleading inferences about the nature and magnitude of any interaction. A range of different parameterisations have been published that could be used with the same fundamental modelling framework that accounts for imperfect detection, although each parameterisation has different advantages and disadvantages.</p> <p>2. We propose a parameterisation based on log-linear modelling that does not require a species hierarchy to be defined (in terms of dominance), and enables a numerically robust approach for estimating covariate effects.</p> <p>3. Conceptually the parameterisation is equivalent to using the presence of species in the current, or a previous, time period as predictor variables for the current occurrence of other species. This leads to natural, 'symmetric', interpretations of parameter estimates.</p> <p>4. The parameterisation can be applied to many species, in either a maximum-likelihood or Bayesian estimation framework. We illustrate the method using camera trapping data collected on three mesocarnivore species in South Texas.</p>
Fig. 1 in Estimating parasite infrapopulation size given imperfect detection: Proof-of-concept with ectoparasitic fleas on prairie dogs
Fig. 1. Left: Frequency histogram of raw (field) flea count indices from prairie dogs. Middle and right: Huggins closed captures model estimates for fleas combed from prairie dogs, including a histogram of estimated flea counts (infrapopulation size = ̂N) and a positive correlation between Julian date and individual flea detection probability (here, p from the first combing occasion within primary trapping occasions). In the histograms, counts of 0 fleas (gray bars) are presented for illustration; those data were not analyzed herein, because the Huggins closed captures models 'condition' on primary occasions with at least 1 flea being detected (black bars). Model output is from the top model in Table 1. On the right, dotted lines are 95% confidence intervals.
Investigating cooccurrence patterns and dynamics for many imperfectly detected species, using a log-linear modelling parameterisation
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Imperfect detection in plant populations can cause misestimates of demographic rates and missed population trends: The case for Astragalus microcymbus Barneby
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Data from: Effectiveness of joint species distribution models in the presence of imperfect detection
<p>Joint species distribution models (JSDMs) are a recent development in biogeography and enable the spatial modelling of multiple species and their interactions and dependencies. However, most models do not consider imperfect detection, which can significantly bias estimates. This is one of the first papers to account for imperfect detection when fitting data with JSDMs and to explore the complications that may arise.</p> <p>A multivariate probit JSDM that explicitly accounts for imperfect detection is proposed, and implemented using a Bayesian hierarchical approach. We investigate the performance of the JSDM in the presence of imperfect detection for a range of factors, including varied levels of detection and species occupancy, and varied numbers of survey sites and replications. To understand how effective this JSDM is in practice, we also compare results to those from a JSDM that does not explicitly model detection but instead makes use of "collapsed data". A case study of owls and gliders in Victoria Australia is also illustrated.</p> <p>Using simulations, we found that the JSDMs explicitly accounting for detection can accurately estimate intrinsic correlation between species with enough survey sites and replications. Reducing the number of survey sites decreases the precision of estimates, while reducing the number of survey replications can lead to biased estimates. For low probabilities of detection, the model may require a large number of survey replications to remove bias from estimates. However, JSDMs not explicitly accounting for detection may have a limited ability to disentangle detection from occupancy, which substantially reduces their ability to accurately infer the species distribution spatially. Our case study showed positive correlation between Sooty Owls and Greater Gliders, despite a low number of survey replications.</p> <p>To avoid biased estimates of inter-species correlations and species distributions, imperfect detection needs to be considered. However, for low probability of detection, the JSDMs explicitly accounting for detection is data hungry. Estimates from such models may still be subject to bias. To overcome the bias, researchers need to carefully design surveys and choose appropriate modelling approaches. The survey design should ensure sufficient survey replications for unbiased inferences on species inter-dependencies and occupancy.</p>
Accounting for imperfect detection in data from museums and herbaria when modeling species distributions: Combining and contrasting data-level versus model-level bias correction
The digitization of museum collections as well as an explosion in citizen science initiatives has resulted in a wealth of data that can be useful for understanding the global distribution of biodiversity, provided that the well-documented biases inherent in unstructured opportunistic data are accounted for. While traditionally used to model imperfect detection using structured data from systematic surveys of wildlife, occupancy models provide a framework for modelling the imperfect collection process that results in digital specimen data. In this study, we explore methods for adapting occupancy models for use with biased opportunistic occurrence data from museum specimens and citizen science platforms using 7 species of Anacardiaceae in Florida as a case study. We explored two methods of incorporating information about collection effort to inform our uncertainty around species presence: (1) filtering the data to exclude collectors unlikely to collect the focal species and (2) incorporating collection covariates (collection type, time of collection, and history of previous detections) into a model of collection probability. We found that the best models incorporated both the background data filtration step as well as collector covariates. Month, method of collection and whether a collector had previously collected the focal species were important predictors of collection probability. Efforts to standardize meta-data associated with data collection will improve efforts for modeling the spatial distribution of a variety of species.
Accounting for imperfect detection in data from museums and herbaria when modeling species distributions: Combining and contrasting data-level versus model-level bias correction
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Data from: Importance of accounting for imperfect detection of plants in the estimation of population growth rates
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Data from: Effectiveness of joint species distribution models in the presence of imperfect detection
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Data from: Imperfect pathogen detection from non-invasive skin swabs biases disease inference
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Data from: The measurement of selection when detection is imperfect: how good are naïve methods?
The life spans of animals can be measured in natural populations by uniquely marking individuals and then releasing them into the field. Selection on survival (a component of fitness) can subsequently be quantified by regressing the life spans of these marked individuals on their trait values. However, marked individuals are not always seen on every subsequent catching occasion, and for this reason, imperfect detection is considered a problem when estimating survival selection in natural populations. Capture–mark–recapture methods have been advocated as a powerful means to correct for imperfect detection. Here, we use simulated and field data sets to evaluate the effect of assuming perfect detection ('naïve methods'), when detection is really imperfect. We compared the performance of the naïve methods with methods correcting for imperfect detection (mark–recapture methods, or MR). Although the effects of trait-dependent recapture probability are mitigated when recapture probability is high, mark–recapture methods still provide the safest choice when recapture probability might be trait-dependent. In our simulations, mark–recapture methods had a power advantage over naïve methods, but all methods lost statistical power at low recapture probabilities. The main advantage of mark–recapture methods over naïve methods is the ability to control for hidden trait-dependent recapture probability, as it is often hard to tell a priori if trait dependence is an issue in a particular study. However, when trait-dependent recapture probability is weak, naïve methods and mark–recapture methods perform similarly as long as recapture rates do not become too low, and the main problem of survival selection studies is still low statistical power. We provide a R package (EasyMARK) alongside with this paper to facilitate future integration between MR methods and classical selection studies. EasyMARK provides the opportunity to convert the regression coefficients from MR-approaches in to classical standardized selection gradients.
Imperfect detection alters the outcome of management strategies for protected areas
<p>Designing protected areas configurations to maximize biodiversity is a critical conservation goal. The configuration of protected areas can significantly impact the richness and identity of the species found there; one large patch supports larger populations but can facilitate competitive exclusion. Conversely, many small habitats spreads risk but may exclude predators that typically require large home ranges. Identifying how best to design protected areas is further complicated by monitoring programs failing to detect species. Here we test the consequences of different protected area configurations using multi-trophic level experimental microcosms. We demonstrate that for a given total size, many small patches generate higher species richness, are more likely to contain predators, and have fewer extinctions compared to single large patches. However, the relationship between the size and number of patches and species richness was greatly affected by insufficient monitoring, and could lead to incorrect conservation decisions, especially for higher trophic levels.</p>
Data from: An integrated occupancy and space-use model to predict abundance of imperfectly detected, territorial vertebrates
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Data from: The measurement of selection when detection is imperfect: how good are naïve methods?
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Imperfect detection alters the outcome of management strategies for protected areas
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Data from: 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.
Data from: Integrated species distribution models: combining presence-background data and site-occupany data with imperfect detection
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Data from: Assessing the impacts of imperfect detection on estimates of diversity and community structure through multispecies occupancy modeling
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