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Applying the N‐mixture model approach to estimate mosquito population absolute abundance from monitoring data
Abstract
Estimating population abundance is a key objective of surveillance programmes, particularly for vector species of public health interest. For mosquitos, which are vectors of human pathogens, established methods to measure absolute population abundance such as mark‐release‐recapture are difficult to implement and usually spatially limited. Typically, regional monitoring schemes assess species relative abundance (counting captured individuals) to prioritize control efforts and study species distribution. However, assessing absolute abundance is crucial when the focus is on pathogen transmission by contacts between vectors and hosts. Here, we applied the N‐mixture model approach to estimate mosquito abundance from standard monitoring data.
We extended the N‐mixture model approach in a Bayesian framework by considering a beta‐binomial distribution for the detection process. We ran a simulation study to explore model performance under a low detection probability, a time‐varying population and different sets of independent variables.
When informative priors were used and the model was well specified, estimates by N‐mixture model well correlated (>0.9) with synthetic data and had a mean absolute deviation of about 20%. Correlation decreased and biased increased with uninformative priors or model misspecification.
When fed with field monitoring data to estimate the absolute abundance of the mosquito arbovirus vector Aedes albopictus within the metropolitan city of Rome (Italy), the N‐mixture model showed higher population size in residential neighbourhoods than in large green areas and revealed that traps located adjacent to vegetated sites have a higher probability of capturing mosquitoes.
Synthesis and applications . Our results show that, if supported by a good knowledge of the target species biology and by informative priors (e.g. from previous studies of capture rates), the N‐mixture model represents a valuable tool to exploit field monitoring data to estimate absolute abundance of disease vectors and to assess vector‐related health risk on a wide spatial and temporal scale. For mosquitoes specifically, it is also valuable to invest in increased efficiency of trapping devices to improve estimates of absolute abundance from the models.
... Here we present an independent evaluation of WIM releases for the suppression of Ae. aegypti. Additionally, we describe, and test, the abundance estimation approach we used (N-Mixture Bayesian hierarchical model [18]); this approach is relatively novel in mosquito ecology, being used previously to estimate the abundance of Ae. albopictus [19], and Ae. aegypti, but with a Mark Release Recapture Component [20] (MRR). ...
... This approach has been previously used in mosquito ecology and has potential applications in analyzing mosquito surveillance data and guide control actions. It was used to estimate the abundance of Ae. albopictus [19], and Ae. aegypti in a Mark Release Recapture (MRR) study [20]. ...
... We discovered during data validation that the trapping time of the first trapping (first day of week 28), in both areas, was statistically different from other weeks. The mean trapping time during the first trapping was 27 (26,27) hrs., and 18 (18,19) hrs. in the UA, and TA respectively. In comparison, the mean trapping time on other days was 23 (23,24) hrs. ...
Among disease vectors, Aedes aegypti (L.) (Diptera: Culicidae) is one of the most insidious species in the world. The disease burden created by this species has dramatically increased in the past 50 years, and during this time countries have relied on pesticides for control and prevention of viruses borne by Ae . aegypti . The small number of available insecticides with different modes of action had led to increases in insecticide resistance, thus, strategies, like the “Incompatible Insect Technique” using Wolbachia ’s cytoplasmic incompatibility are desirable.
We evaluated the effect of releases of Wolbachia infected Ae . aegypti males on populations of wild Ae . aegypti in the metropolitan area of Houston, TX. Releases were conducted by the company MosquitoMate, Inc. To estimate mosquito population reduction, we used a mosquito abundance Bayesian hierarchical estimator that accounted for inefficient trapping. MosquitoMate previously reported a reduction of 78% for an intervention conducted in Miami, FL. In this experiment we found a reduction of 93% with 95% credibility intervals of 86% and 96% after six weeks of continual releases. A similar result was reported by Verily Life Sciences, 96% [94%, 97%], in releases made in Fresno, CA.
... N-mixture models have already been applied to a wide range of wildlife species (e.g. Belant et al., 2016;Hunter, Nibbelink & Cooper, 2017;Kéry, 2018;Kidwai et al., 2019;Manica et al., 2019;Romano et al., 2017;Ward et al., 2017), but are still considered as an emerging framework with ongoing extensions to original parameterizations (Barker et al., 2018;Bötsch, Jenni & Kéry, 2019;Denes, Silveira & Beissinger, 2015;. ...
... Allowing for the separate estimation of abundance and detection probabilities from replicated counts of unmarked individuals (e.g. Zipkin et al., 2014), N-mixture models have in recent years become applied to taxa ranging from mosquitoes to megafaunal mammals (Kidwai et al., 2019;Manica et al., 2019). In the present study, we applied a set of such models to spotlight count data for C. moreletii in southern Yucatan, where it inhabits particularly dynamic waterbodies and serves as an important flagship species for a large expanse of protected forest. ...
Estimates of animal abundance provide essential information for population ecological studies. However, the recording of individuals in the field can be challenging, and accurate estimates require analytical techniques which account for imperfect detection. Here, we quantify local abundances and overall population size of Morelet's crocodiles (Crocodylus moreletii) in the region of Calakmul (Campeche, Mexico), comparing traditional approaches for crocodylians (Minimum Population Size-MPS; King's Visible Fraction Method-VFM) with binomial N-mixture models based on Poisson, zero-inflated Poisson (ZIP) and negative binomial (NB) distributions. A total of 191 nocturnal spotlight surveys were conducted across 40 representative locations (hydrologically highly dynamic aquatic sites locally known as aguadas) over a period of 3 years (2017-2019). Local abundance estimates revealed a median of 1 both through MPS (min-max: 0-89; first and third quartiles, Q 1-Q 3 : 0-7) and VFM (0-112; Q 1-Q 3 : 0-9) non-hatchling C. moreletii for each aguada, respectively. The ZIP based N-mixture approach shown overall superior confidence over Poisson and NB, and revealed a median of 6 ± 3 individuals (min = 0; max = 120 ± 18; Q 1 = 0; Q 3 = 18 ± 4) jointly with higher detectabilities in drying aguadas with low and intermediate vegetation cover. Extrapolating these inferences across all waterbodies in the study area yielded an estimated~10,000 (7,000-11,000) C. moreletii present, highlighting Calakmul as an important region for this species. Because covariates enable insights into population responses to local environmental conditions, N-mixture models applied to spotlight count data result in particularly insightful estimates of crocodylian detection and abundance.
... N-mixture models have been applied to a wide range of wildlife species ranging from invertebrates to large mammals (Paudel et al., 2015;Kidwai et al., 2019;Manica et al., 2019;Searle et al., 2020;Than et al. 2020;Barão-Nóbrega et al., 2022), that explicitly allow for assessing abundance estimates and detection probability from a repeated count data of unmarked individuals (Kéry and Royle, 2015;Bötsch et al., 2020). N-mixture model is cost effective and widely used to estimate the abundance of population from count data with both spatial and temporal replications while accounting for imperfect detection (Royle, 2004;Joseph et al., 2009). ...
Globally, the river ecosystems are threatened due to human-driven exploitation and indiscriminate resource use. The rate of species loss is a magnitude higher in these ecosystems, hence, identifying conservation priority areas as refugia, using the flagship-cum-indicator species approach can aid in long-term conservation of multiple species and ensure uninterrupted functioning of ecological processes. For effective conservation planning, we derived the site occupancy and abundance of Gangetic dolphin (Platanista gangetica) as a flagship species in the Ganga River Basin, and modelled their distribution vis-à-vis river conditions for identifying Conservation Priority Stretches (CPS). The study incorporates the first-ever basin-wide (4635 km river) Gangetic dolphin (GD) sightings to estimate range decline, abundance, and identify CPS of select rivers in the Basin. A total of 2151 sightings of surfacing dolphins with mean encounter rate of 0.55 ± 0.09 sightings/km of the river was observed from the surveyed stretch. The GD encounter rate varied significantly across the surveyed rivers (Analysis of Variance, F = 3.08, p
... An analogous relationship between urban habitats and distribution was found for another alien insect, the Asian tiger mosquito (Aedes albopictus (Skuse, 1894) (Diptera: Culicidae)). This species has rapidly adapted to the newly invaded areas but has never been observed using spontaneous trees as oviposition sites because it prefers sub-pots or tires that are typical cavities in the urban areas [55]. The strong connection between C. marshalli and its food plant was confirmed by the high contribution of the pel_abu variable in explaining both C. marshalli occurrence and egg abundance, even with low levels of Pelargonium abundance. ...
Simple Summary
Cacyreus marshalli is strictly dependent on its host plant (Pelargonium spp.), which is widely cultivated as an ornamental plant in mountain areas. An experiment demonstrated that the butterfly is able to develop on some wild geraniums, too, making mountain areas highly at risk for a potential expansion to natural habitats. We therefore decided to carry out research in a protected mountain area (Gran Paradiso National Park), focusing on the drivers which determine the distribution of C. marshalli using data provided by either an opportunistic approach or a rigorous survey protocol. The data collected via the planned survey were more informative than the opportunistic observations, which were few and narrow. We suggest investing more in citizen science projects and combining them with a designed protocol according to an integrated approach. We observed that C. marshalli distribution is strictly linked to host plant availability but is constrained by cold temperatures, although Pelargonium spp. are abundant. The temperature increase scenario showed an increase of butterfly abundance, but halving of the host plant population could drive the rate of infestation to return to what it was previously, excluding a countertrend in some high-altitude sites. It is therefore important to test management actions designed to control alien species before implementing them.
Abstract
Cacyreus marshalli is the only alien butterfly in Europe. It has recently spread in the Gran Paradiso National Park (GPNP), where it could potentially compete with native geranium-consuming butterflies. Our study aimed to (1) assess the main drivers of its distribution, (2) evaluate the potential species distribution in GPNP and (3) predict different scenarios to understand the impact of climate warming and the effect of possible mitigations. Considering different sampling designs (opportunistic and standardised) and different statistical approaches (MaxEnt and N-mixture models), we built up models predicting habitat suitability and egg abundance for the alien species, testing covariates as bioclimatic variables, food plant (Pelargonium spp.) distribution and land cover. A standardised approach resulted in more informative data collection due to the survey design adopted. Opportunistic data could be potentially informative but a major investment in citizen science projects would be needed. Both approaches showed that C. marshalli is associated with its host plant distribution and therefore confined in urban areas. Its expansion is controlled by cold temperatures which, even if the host plant is abundant, constrain the number of eggs. Rising temperatures could lead to an increase in the number of eggs laid, but the halving of Pelargonium spp. populations would mostly mitigate the trend, with a slight countertrend at high elevations.
... The results of the spatial analysis for the whole set of data ( Figure S4) show that (i) the no mosquito category is more frequent than the other categories at higher elevations, reflecting the less permissive eco-climatic situation beyond 500 m [24,25]; (ii) the high abundance categories are more frequent in areas without full artificial surface cover, in agreement with recognised hot-spots in small green areas within a highly urbanized environment in Italy [6,26]; (iii) the high abundance categories are more frequent in areas closer to the coastline; notably, although no entomological data are available to confirm this results, it is noteworthy that all outbreaks of chikungunya in Italy started in coastal sites [4]; (iv) lack of clear distribution patterns related to distance from inland water bodies. ...
Mosquitoes represent a considerable nuisance and are actual/potential vectors of human diseases in Europe. Costly and labour-intensive entomological monitoring is needed to correct planning of interventions aimed at reducing nuisance and the risk of pathogen transmission. The widespread availability of mobile phones and of massive Internet connections opens the way to the contribution of citizen in complementing entomological monitoring. ZanzaMapp is the first mobile “mosquito” application for smartphones specifically designed to assess citizens’ perception of mosquito abundance and nuisance in Italy. Differently from other applications targeting mosquitoes, ZanzaMapp prioritizes the number of records over their scientific authentication by requesting users to answer four simple questions on perceived mosquito presence/abundance/nuisance and geo-localizing the records. The paper analyses 36,867 ZanzaMapp records sent by 13,669 devices from 2016 to 2018 and discusses the results with reference to either citizens’ exploitation and appreciation of the app and to the consistency of the results obtained with the known biology of main mosquito species in Italy. In addition, we provide a first small-scale validation of ZanzaMapp data as predictors of Aedes albopictus biting females and examples of spatial analyses and maps which could be exploited by public institutions and administrations involved in mosquito and mosquito-borne pathogen monitoring and control.
Mosquito surveillance data can be used for predicting mosquito distribution and dynamics as they relate to human disease. Often these data are collected by independent agencies and aggregated to state and national level portals to characterize broad spatial and temporal dynamics. These larger repositories may also share the data for use in mosquito and/or disease prediction and forecasting models. Assumed, but not always confirmed, is consistency of data across agencies. Subtle differences in reporting may be important for development and the eventual interpretation of predictive models. Using mosquito vector surveillance data from Arizona as a case study, we found differences among agencies in how trapping practices were reported. Inconsistencies in reporting may interfere with quantitative comparisons if the user has only cursory familiarity with mosquito surveillance data. Some inconsistencies can be overcome if they are explicit in the metadata while others may yield biased estimates if they are not changed in how data are recorded. Sharing of metadata and collaboration between modelers and vector control agencies is necessary for improving the quality of the estimations. Efforts to improve sharing, displaying, and comparing vector data from multiple agencies are underway, but existing data must be used with caution.
Surveillance programs are needed to guide mosquito-control operations to reduce both nuisance and the spread of mosquito-borne diseases. Understanding the thresholds for action to reduce both nuisance and the risk of arbovirus transmission is becoming critical. To date, mosquito surveillance is mainly implemented to inform about pathogen transmission risks rather than to reduce mosquito nuisance even though lots of control efforts are aimed at the latter. Passive surveillance, such as digital monitoring (validated by entomological trapping), is a powerful tool to record biting rates in real time.
High-quality data are essential to model the risk of arbovirus diseases. For invasive pathogens, efforts are needed to predict the arrival of infected hosts linked to the small-scale vector to host contact ratio, while for endemic pathogens efforts are needed to set up region-wide highly-structured surveillance measures to understand seasonal re-activation and pathogen transmission in order to carry out effective control operations.
A large chikungunya outbreak is ongoing in Italy, with a main cluster in the Anzio coastal municipality. With preliminary epidemiological data, and a transmission model using mosquito abundance and biting rates, we estimated the basic reproduction number R0 at 2.07 (95% credible interval: 1.47–2.59) and the first case importation between 21 May and 18 June 2017. Outbreak risk was higher in coastal/rural sites than urban ones. Novel transmission foci could occur up to mid-November.
Background
Experiments involving mosquito mark-release-recapture (MRR) design are helpful to determine abundance, survival and even recruitment of mosquito populations in the field. Obstacles in mosquito MRR protocols include marking limitations due to small individual size, short lifespan, low efficiency in capturing devices such as traps, and individual removal upon capture. These limitations usually make MRR analysis restricted to only abundance estimation or a combination of abundance and survivorship, and often generate a great degree of uncertainty about the estimations.
Methodology/Principal findings
We present a set of Bayesian biodemographic models designed to fit data from most common mosquito recapture experiments. Using both field data and simulations, we consider model features such as capture efficiency, survival rates, removal of individuals due to capturing, and collection of pupae. These models permit estimation of abundance, survivorship of both marked and unmarked mosquitoes, if different, and recruitment rate. We analyze the accuracy of estimates by varying the number of released individuals, abundance, survivorship, and capture efficiency in multiple simulations. These methods can stand capture efficiencies as low as usually reported but their accuracy depends on the number of released mosquitoes, abundance and survivorship. We also show that gathering pupal counts allows estimating differences in survivorship between released mosquitoes and the unmarked population.
Conclusion/Significance
These models are important both to reduce uncertainty in evaluating MMR experiments and also to help planning future MRR studies.
Successful management of wildlife populations requires accurate estimates of abundance. Abundance estimates can be confounded by imperfect detection during wildlife surveys. N-mixture models enable quantification of detection probability and often produce abundance estimates that are less biased. The purpose of this study was to demonstrate the use of the R-INLA package to analyze N-mixture models and to compare performance of R-INLA to two other common approaches -- JAGS (via the runjags package), which uses Markov chain Monte Carlo and allows Bayesian inference, and unmarked, which uses Maximum Likelihood and allows frequentist inference. We show that R-INLA is an attractive option for analyzing N-mixture models when (1) familiar model syntax and data format (relative to other R packages) are desired, (2) survey level covariates of detection are not essential, (3) fast computing times are necessary (R-INLA is 10 times faster than unmarked, 300 times faster than JAGS), and (4) Bayesian inference is preferred.
A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics * Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants) * Development of classical, likelihood-based procedures for inference, as well as Bayesian methods of analysis * Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS * Computing support in technical appendices in an online companion web site.
Aedes albopictus is a tropical invasive species which in the last decades spread worldwide, also colonizing temperate regions of Europe and US, where it has become a public health concern due to its ability to transmit exotic arboviruses, as well as severe nuisance problems due to its aggressive daytime outdoor biting behaviour. While several studies have been carried out in order to predict the potential limits of the species expansions based on eco-climatic parameters, few studies have so far focused on the specific effects of these variables in shaping its micro-geographic abundance and dynamics. The present study investigated eco-climatic factors affecting Ae. albopictus abundance and dynamics in metropolitan and sub-urban/rural sites in Rome (Italy), which was colonized in 1997 and is nowadays one of the most infested metropolitan areas in Southern Europe. To this aim, longitudinal adult monitoring was carried out along a 70 km-transect across and beyond the most urbanized and densely populated metropolitan area. Two fine scale spatiotemporal datasets (one with reference to a 20m circular buffer around sticky traps used to collect mosquitoes and the second to a 300m circular buffer within each sampling site) were exploited to analyze the effect of climatic and socio-environmental variables on Ae. albopictus abundance and dynamics along the transect. Results showed an association between highly anthropized habitats and high adult abundance both in metropolitan and sub-urban/rural areas, with “small green islands” corresponding to hot spots of abundance in the metropolitan areas only, and a bimodal seasonal dynamics with a second peak of abundance in autumn, due to heavy rains occurring in the preceding weeks in association with permissive temperatures. The results provide useful indications to prioritize public mosquito control measures in temperate urban areas where nuisance, human-mosquito contact and risk of local arbovirus transmission are likely higher, and highlight potential public health risks also after the summer months typically associated with high mosquito densities
The key operation in Bayesian inference, is to compute high-dimensional integrals. An old approximate technique is the Laplace method or approximation, which dates back to Pierre- Simon Laplace (1774). This simple idea approximates the integrand with a second order Taylor expansion around the mode and computes the integral analytically. By developing a nested version of this classical idea, combined with modern numerical techniques for sparse matrices, we obtain the approach of Integrated Nested Laplace Approximations (INLA) to do approximate Bayesian inference for latent Gaussian models (LGMs). LGMs represent an important model-abstraction for Bayesian inference and include a large proportion of the statistical models used today. In this review, we will discuss the reasons for the success of the INLA-approach, the R-INLA package, why it is so accurate, why the approximations are very quick to compute and why LGMs make such a useful concept for Bayesian computing.
Ecosystem specialists are predicted to be more vulnerable to global change than generalists, but whether specialists within an ecosystem will respond similarly to those changes is often largely unknown. Will specialists track changes in their habitats as a group, or are their distributions governed by landscape gradients that will make some species more sensitive to habitat changes? In this study, we forecasted the effects of sea level rise (SLR) on two salt marsh specialist bird species: clapper rails Rallus crepitans and seaside sparrows Ammodramus maritimus. We sampled the abundance of these two species in salt marshes throughout the Georgia, USA, coast in 2013–2014, and analyzed count data using a Bayesian N-mixture model. Model predictions were applied to an SLR land cover model to determine distribution shifts over 100 years. Both species distributions were most sensitive to the relative elevation gradient, with clapper rails using lower elevation marshes and seaside sparrows using higher elevation marshes. These disparities in habitat use, along with other differences according to marsh salinity and distance to forested areas, led to divergent responses to SLR. Clapper rail habitat is predicted to increase with SLR by 52%, but seaside sparrow habitat will contract by 81% by the year 2100. Seaside sparrow habitat is not predicted to decline until sometime between 2025 and 2050, at which point the decline will rapidly accelerate, indicating the importance of careful monitoring in future decades. Diverging responses to a global perturbation create a conservation planning dilemma: if specialists have opposing responses to SLR, it may be difficult to manage conservation areas that accommodate many species.
N‐mixture models have become a popular method for estimating abundance of free‐ranging animals that are not marked or identified individually. These models have been used on count data for single species that can be identified with certainty. However, co‐occurring species often look similar during one or more life stages, making it difficult to assign species for all recorded captures. This uncertainty creates problems for estimating species‐specific abundance, and it can often limit life stages to which we can make inference.
We present a new extension of N‐mixture models that accounts for species uncertainty. In addition to estimating site‐specific abundances and detection probabilities, this model allows estimating probability of correct assignment of species identity. We implement this hierarchical model in a Bayesian framework and provide all code for running the model in BUGS language programs.
We present an application of the model on count data from two sympatric freshwater fishes, the brook stickleback ( Culaea inconstans ) and the ninespine stickleback ( Pungitius pungitius ), and illustrate implementation of covariate effects (habitat characteristics). In addition, we used a simulation study to validate the model and illustrate potential sample size issues. We also compared, for both real and simulated data, estimates provided by our model to those obtained by a simple N‐mixture model when captures of unknown species identification were discarded. In the latter case, abundance estimates appeared highly biased and very imprecise, while our new model provided unbiased estimates with higher precision.
This extension of the N‐mixture model should be useful for a wide variety of studies and taxa, as species uncertainty is a common issue. It should notably help improve investigation of abundance and vital rate characteristics of organisms’ early life stages, which are sometimes more difficult to identify than adults.
Strategies to minimize dengue transmission commonly rely on vector control, which aims to maintain Ae. aegypti density below a theoretical threshold. Mosquito abundance is traditionally estimated from mark-release-recapture (MRR) experiments, which lack proper analysis regarding accurate vector spatial distribution and population density. Recently proposed strategies to control vector-borne diseases involve replacing the susceptible wild population by genetically modified individuals' refractory to the infection by the pathogen. Accurate measurements of mosquito abundance in time and space are required to optimize the success of such interventions. In this paper, we present a hierarchical probabilistic model for the estimation of population abundance and spatial distribution from typical mosquito MRR experiments, with direct application to the planning of these new control strategies. We perform a Bayesian analysis using the model and data from two MRR experiments performed in a neighborhood of Rio de Janeiro, Brazil, during both low- and high-dengue transmission seasons. The hierarchical model indicates that mosquito spatial distribution is clustered during the winter (0.99 mosquitoes/premise 95% CI: 0.80-1.23) and more homogeneous during the high abundance period (5.2 mosquitoes/premise 95% CI: 4.3-5.9). The hierarchical model also performed better than the commonly used Fisher-Ford's method, when using simulated data. The proposed model provides a formal treatment of the sources of uncertainty associated with the estimation of mosquito abundance imposed by the sampling design. Our approach is useful in strategies such as population suppression or the displacement of wild vector populations by refractory Wolbachia-infected mosquitoes, since the invasion dynamics have been shown to follow threshold conditions dictated by mosquito abundance. The presence of spatially distributed abundance hotspots is also formally addressed under this modeling framework and its knowledge deemed crucial to predict the fate of transmission control strategies based on the replacement of vector populations.
Inference and estimates of abundance are critical for quantifying population dynamics and impacts of environmental change. Yet imperfect detection and other phenomena that cause zero inflation can induce estimation error and obscure ecological patterns.
Recent statistical advances provide an increasingly diverse array of analytical approaches for estimating population size to address these phenomena.
We examine how detection error and zero inflation in count data inform the choice of analytical method for estimating population size of unmarked individuals that are not uniquely identified. We review two established ( GLM s and distance sampling) and nine emerging methods that use N ‐mixture models (Royle–Nichols model, and basic, zero inflated, temporary emigration, beta‐binomial, generalized open‐population, spatially explicit, single visit and multispecies) to estimate abundance of unmarked populations, focusing on their requirements and how each method accounts for imperfect detection and zero inflation.
Eight of the emerging methods can account for both imperfect detection and additional variation in population size in the forms of non‐occupancy, temporary emigration, correlated detection and population dynamics.
Methods differ in sampling design requirements (e.g. count vs. detection/non‐detection data, single vs. multiple visits, covariate data), and their suitability for a particular study will depend on the characteristics of the study species, scale and objectives of the study, and financial and logistical considerations.
Most emerging methods were developed over the past decade, so their efficacy is still under study, and additional statistical advances are likely to occur.
Temperature is a main driver for most ecological processes, and temperature time series provide key environmental indicators for various applications and research fields. High spatial and temporal resolutions are crucial for detailed analyses in various fields of research. A disadvantage of temperature data obtained by satellites is the occurrence of gaps that must be reconstructed. Here, we present a new method to reconstruct high-resolution land surface temperature (LST) time series at the continental scale gaining 250-m spatial resolution and four daily values per pixel. Our method constitutes a unique new combination of weighted temporal averaging with statistical modeling and spatial interpolation. This newly developed reconstruction method has been applied to greater Europe, resulting in complete daily coverage for eleven years. To our knowledge, this new reconstructed LST time series exceeds the level of detail of comparable reconstructed LST datasets by several orders of magnitude. Studies on emerging diseases, parasite risk assessment and temperature anomalies can now be performed on the continental scale, maintaining high spatial and temporal detail. We illustrate a series of applications in this paper. Our dataset is available online for download as time aggregated derivatives for direct usage in GIS-based applications (Article PDF: http://www.mdpi.com/2072-4292/6/5/3822)
Our ultimate objective is to design cost-effective control strategies for Aedes albopictus, the Asian tiger mosquito, an important urban nuisance and disease vector that expanded worldwide during the last 40 years. We conducted mosquito larval surveys from May through October 2009 in the City of Trenton, New Jersey, USA, while performing intensive monthly source-reduction campaigns that involved removing, emptying, or treating all accessible containers with larvicides and pupicides. We examined patterns of occurrence of Ae. albopictus and Culex pipiens, another urban mosquito, among different container types by comparing observed and expected number of positive containers of each type. Expected use was based on the relative frequency of each container type in the environment. Aedes albopictus larvae and pupae were found significantly more often than expected in medium volumes of water in buckets and plant saucers but were rarely collected in small volumes of water found in trash items such as discarded cups and cans. They were also absent from large volumes of water such as in abandoned swimming pools and catch basins, although we consistently collected Cx. pipiens from those habitats. The frequency of Ae. albopictus in tires indicated rapid and extensive use of these ubiquitous urban containers. Standard larval-based indices did not correlate with adult catches in BG-Sentinel traps, but when based only on Ae. albopictus key containers (buckets, plant saucers, equipment with pockets of water, and tires) they did. Although we found that only 1.2% of the 20,039 water-holding containers examined contained immature Ae. albopictus (5.3% if only key containers were counted), adult populations were still above nuisance action thresholds six times during the 2009 mosquito season. We conclude that in urban New Jersey, effective source reduction for Ae. albopictus control will require scrupulous and repeated cleaning or treatment of everyday use containers and extensive homeowner collaboration.
Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientific questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mechanisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unified modeling interface. The R package unmarked provides such a unified modeling framework, including tools for data exploration, model fitting, model criticism, post-hoc analysis, and model comparison.
Accurate estimation of population size is key to understanding the ecology of disease vectors, as well as the epidemiology of the pathogens they carry and to plan effective control activities. Population size can be estimated through mark-Release-Recapture (MRR) experiments that are based on the assumption that the ratio of recaptured individuals to the total captures approximates the ratio of marked individuals released to the total population. However, methods to obtain population size estimates usually consider pooled data and are often based on the total number of marked and unmarked captures. We here present a logistic regression model, based on the principle of the well-known Fisher-Ford method, specific for MRR experiments where the information available is the number of marked mosquitoes released, the number of marked and unmarked mosquitoes caught in each trap and on each day, and the geographic coordinates of the traps. The model estimates population size, taking into consideration the distance between release points and traps, the time between release and recapture, and the loss of marked mosquitoes to death or dispersal. The performance and accuracy of the logistic regression model has been assessed using simulated data from known population sizes. We then applied the model to data from MRR experiments with Aedes albopictus Skuse performed on the campus of “Sapienza” University in Rome (Italy).
The Biogents® Sentinel (BGS) trap is the standard tool to monitor adult Aedes (Stegomyia) albopictus (Skuse) (Diptera: Culicidae), the Asian tiger mosquito. BGS traps are commonly placed in residential properties during surveillance operations, but locations within properties may have significant differences in ambient light, temperature, and humidity (e.g. between a sunlit lawn and shady underbrush). We examined the effect of BGS trap placement on Ae. albopictus capture rates in three residential properties in Monmouth County, New Jersey, USA. In each property we visually selected locations as shade, partial shade, and sun. Traps in "partial shade" locations were under vegetation and were exposed to filtered sunlight during some parts of the day while "shaded" locations were never exposed to direct sunlight. Locations defined as "sun" were exposed to direct sunlight for large parts of the day. We placed a BGS trap in each of the three location types and used small data loggers to measure temperature, relative humidity, and light exposure at each trap during a 24-hour deployment. To address temporal variability, we made seven separate measurements from 31 August to 22 September 2010. We found that "partial shade" and "full shade" locations did not differ but that "full sun" locations had significantly higher light exposure, higher temperature, and lower humidity. Importantly, Ae. albopictus catches (males, females, or both) were consistently and significantly over 3 times higher in traps located in shaded locations. To further investigate the effects of local temperature and humidity on surveillance we examined Ae. albopictus collections from 37 BGS traps fitted with data loggers and deployed weekly from August through mid October, during the 2009 season, in three urban sites in Mercer County, NJ. We confirmed that local climate influences capture rates and that Ae. albopictus surveillance projects need to monitor trap placement carefully for maximum efficiency.
Aedes albopictus (Skuse) and Ae. japonicus (Theobald) are important container-inhabiting mosquitoes that transmit disease agents, outcompete native species, and continue to expand their range in the United States. Both species deposit eggs in natural and artificial containers and thrive in peridomestic environments. The goal of our study was to examine the types and characteristics of containers that are most productive for these species in the northeastern United States. In total, 306 containers were sampled in urban, suburban, and rural areas of New Jersey. Multiple biotic and abiotic factors were recorded in an attempt to identify variables associated with the productivity of each species. Based on pupal abundance and density of container types, results showed that tires, trash cans, and planter dishes were the most important containers for Ae. albopictus, while planter dishes were the most important containers for Ae. japonicus. Container color (black and gray), material (rubber), and type (tires) were correlated with species presence for Ae. albopictus and Ae. japonicus. These factors may play a role in the selection of oviposition sites by female mosquitoes or in the survival of their progeny. Differences in species composition and abundance were detected between areas classified as urban, suburban, and rural. In urban and suburban areas, Ae. albopictus was more abundant in container habitats than Ae. japonicus; however, Ae. japonicus was more abundant in rural areas, and when water temperatures were below 14 degrees C. Our results suggest many variables can influence the presence of Ae. albopictus and Ae. japonicus in container habitats in northeastern United States.
Predicting abundance across a species' distribution is useful for studies of ecology and biodiversity management. Modeling of survey data in relation to environmental variables can be a powerful method for extrapolating abundances across a species' distribution and, consequently, calculating total abundances and ultimately trends. Research in this area has demonstrated that models of abundance are often unstable and produce spurious estimates, and until recently our ability to remove detection error limited the development of accurate models. The N-mixture model accounts for detection and abundance simultaneously and has been a significant advance in abundance modeling. Case studies that have tested these new models have demonstrated success for some species, but doubt remains over the appropriateness of standard N-mixture models for many species. Here we develop the N-mixture model to accommodate zero-inflated data, a common occurrence in ecology, by employing zero-inflated count models. To our knowledge, this is the first application of this method to modeling count data. We use four variants of the N-mixture model (Poisson, zero-inflated Poisson, negative binomial, and zero-inflated negative binomial) to model abundance, occupancy (zero-inflated models only) and detection probability of six birds in South Australia. We assess models by their statistical fit and the ecological realism of the parameter estimates. Specifically, we assess the statistical fit with AIC and assess the ecological realism by comparing the parameter estimates with expected values derived from literature, ecological theory, and expert opinion. We demonstrate that, despite being frequently ranked the “best model” according to AIC, the negative binomial variants of the N-mixture often produce ecologically unrealistic parameter estimates. The zero-inflated Poisson variant is preferable to the negative binomial variants of the N-mixture, as it models an ecological mechanism rather than a statistical phenomenon and generates reasonable parameter estimates. Our results emphasize the need to include ecological reasoning when choosing appropriate models and highlight the dangers of modeling statistical properties of the data. We demonstrate that, to obtain ecologically realistic estimates of abundance, occupancy and detection probability, it is essential to understand the sources of variation in the data and then use this information to choose appropriate error distributions. Copyright ESA. All rights reserved.
The Asian tiger mosquito, Aedes albopictus, has colonized nearly all the regions of Italy as well as other areas of Europe. During the summer of 2007 the tiger mosquito was responsible for an outbreak of Chikungunya in Italy, when this virus was brought in by a tourist of Indian origin returning from an endemic area. To increase the knowledge of tiger mosquito population dynamics, a survey was carried out from April to November 2008 in the municipalities of Arco and Riva del Garda (northern Italy) through a Biogents Sentinel™ (BG)-trap sampling. In particular, the aim of the study was to evaluate the influence of temperature and rainfall on the activity and dynamics of A. albopictus host-seeking females. The seasonal emergence of host-seeking females was strongly influenced by the minimum temperature, and a lower threshold of 13°C was identified. In addition, the threshold for the end of adult activity was found at a minimum temperature of 9°C. Host-seeking female abundance was positively affected by the accumulated temperatures over the period 3 and 4 weeks before the sampling week, possibly as a consequence of the positive effect of accumulated temperatures on larval density. Instead, accumulated precipitation over 1-4 weeks before sampling was negatively correlated with host-seeking female abundance. Finally, the activity of host-seeking females, estimated by the weekly increment in female abundance, was positively affected by the total abundance of females and by mean weekly temperatures. Our study provides useful information for predicting the dynamics of host-seeking Ae. albopictus females in northern Italy and for designing control strategies for preventing arbovirus outbreaks in areas colonized by Ae. albopictus.
Knowledge of the frequency of contact between a mosquito species and its different hosts is essential to understand the role of each vector species in the transmission of diseases to humans and/or animals. However, no data are so far available on the feeding habits of Aedes albopictus in Italy or in other recently colonized temperate regions of Europe, due to difficulties in collecting blood-fed females of this diurnal and exophilic species. We analyzed Ae. albopictus host-feeding patterns in two urban and two rural sites within the area of Rome (Italy). Ae. albopictus was collected using sticky-traps and the blood-meal origin of 303 females was determined by direct dot-ELISA. The blood-fed sample, although representing only 4% of the total Ae. albopictus collected, demonstrates the useful application of sticky-trap in studying the feeding behavior of the species. The human blood index was significantly different among sites, ranging from 79-96% in urban sites to 23-55% in rural sites, where horses and bovines represented the most bitten hosts. The results obtained confirm the plastic feeding behavior shown by Ae. albopictus in its original range of distribution and highlights the high potential of this species as a vector of human pathogens in urban areas of Italy, where both humans and the mosquito itself may reach very high densities.
N‐mixture models provide an appealing alternative to mark‐recapture models, in that they allow for estimation of detection probability and population size from count data, without requiring that individual animals be identified. There is, however, a cost to using the N‐mixture models: inference is very sensitive to the model's assumptions. We consider the effects of three violations of assumptions which might reasonably be expected in practice: double counting, unmodeled variation in population size over time, and unmodeled variation in detection probability over time. These three examples show that small violations of assumptions can lead to large biases in estimation. The violations of assumptions we consider are not only small qualitatively, but are also small in the sense that they are unlikely to be detected using goodness‐of‐fit tests. In cases where reliable estimates of population size are needed, we encourage investigators to allocate resources to acquiring additional data, such as recaptures of marked individuals, for estimation of detection probabilities. This article is protected by copyright. All rights reserved.
Monitoring animal populations is central to wildlife and fisheries management, and the use of N-mixture models toward these efforts has markedly increased in recent years. Nevertheless, relatively little work has evaluated estimator performance when basic assumptions are violated. Moreover, diagnostics to identify when bias in parameter estimates from N-mixture models is likely is largely unexplored. We simulated count data sets using 837 combinations of detection probability, number of sample units, number of survey occasions, and type and extent of heterogeneity in abundance or detectability. We fit Poisson N-mixture models to these data, quantified the bias associated with each combination, and evaluated if the parametric bootstrap goodness-of-fit (GOF) test can be used to indicate bias in parameter estimates. We also explored if assumption violations can be diagnosed prior to fitting N-mixture models. In doing so, we propose a new model diagnostic, which we term the quasi-coefficient of variation (QCV). N-mixture models performed well when assumptions were met and detection probabilities were moderate (i.e., ≥0.3), and the performance of the estimator improved with increasing survey occasions and sample units. However, the magnitude of bias in estimated mean abundance with even slight amounts of unmodeled heterogeneity was substantial. The parametric bootstrap GOF test did not perform well as a diagnostic for bias in parameter estimates when detectability and sample sizes were low. The results indicate the QCV is useful to diagnose potential bias and that potential bias associated with unidirectional trends in abundance or detectability can be diagnosed using Poisson regression. This study represents the most thorough assessment to date of assumption violations and diagnostics when fitting N-mixture models using the most commonly implemented error distribution. Unbiased estimates of population state variables are needed to properly inform management decision making. Therefore, we also discuss alternative approaches to yield unbiased estimates of population state variables using similar data types, and we stress that there is no substitute for an effective sample design that is grounded upon well-defined management objectives.
Binomial N-mixture models have proven very useful in ecology, conservation and monitoring: they allow estimation and modeling of abundance separately from detection probability using simple counts. Recently, doubts about parameter identifiability have been voiced. I conducted a large-scale screening test with 137 bird data sets from 2,037 sites. I found virtually no identifiability problems for Poisson and zero-inflated Poisson (ZIP) binomial N-mixture models, but negative-binomial (NB) models had problems in 25% of all data sets. The corresponding multinomial N-mixture models had no problems. Parameter estimates under Poisson and ZIP binomial and multinomial N-mixture models were extremely similar. Identifiability problems became a little more frequent with smaller sample sizes (267 and 50 sites), but were unaffected by whether the models did or did not include covariates. Hence, binomial N-mixture model parameters with Poisson and ZIP mixtures typically appeared identifiable. In contrast, NB mixtures were often unidentifiable, which is worrying since these were often selected by AIC. Identifiability of binomial N-mixture models should always be checked. If problems are found, simpler models, integrated models which combine different observation models or the use of external information via informative priors or penalized likelihoods may help. This article is protected by copyright. All rights reserved.
N-mixture models describe count data replicated in time and across sites in terms of abundance N and detectability p. They are popular because they allow inference about N while controlling for factors that influence p without the need for marking animals. Using a capture-recapture perspective, we show that the loss of information that results from not marking animals is critical, making reliable statistical modeling of N and p problematic using just count data. One cannot reliably fit a model in which the detection probabilities are distinct among repeat visits as this model is overspecified. This makes uncontrolled variation in p problematic. By counter example, we show that even if p is constant after adjusting for covariate effects (the "constant p" assumption) scientifically plausible alternative models in which N (or its expectation) is non-identifiable or does not even exist as a parameter, lead to data that are practically indistinguishable from data generated under an N-mixture model. This is particularly the case for sparse data as is commonly seen in applications. We conclude that under the constant p assumption reliable inference is only possible for relative abundance in the absence of questionable and/or untestable assumptions or with better quality data than seen in typical applications. Relative abundance models for counts can be readily fitted using Poisson regression in standard software such as R and are sufficiently flexible to allow controlling for p through the use covariates while simultaneously modeling variation in relative abundance. If users require estimates of absolute abundance, they should collect auxiliary data that help with estimation of p.
Declining populations of large carnivores worldwide, and the complexities of managing human-carnivore conflicts, require accurate population estimates of large carnivores to promote their long-term persistence through well-informed management We used N-mixture models to estimate lion (Panthera leo) abundance from call-in and track surveys in southeastern Serengeti National Park, Tanzania. Because of potential habituation to broadcasted calls and social behavior, we developed a hierarchical observation process within the N-mixture model conditioning lion detectability on their group response to call-ins and individual detection probabilities. We estimated 270 lions (95% credible interval = 170–551) using call-ins but were unable to estimate lion abundance from track data. We found a weak negative relationship between predicted track density and predicted lion abundance from the call-in surveys. Luminosity was negatively correlated with individual detection probability during call-in surveys. Lion abundance and track density were influenced by landcover, but direction of the corresponding effects were undetermined. N-mixture models allowed us to incorporate multiple parameters (e.g., landcover, luminosity, observer effect) influencing lion abundance and probability of detection directly into abundance estimates. We suggest that N-mixture models employing a hierarchical observation process can be used to estimate abundance of other social, herding, and grouping species.
Scientific investigation is of value only insofar as relevant results are obtained and communicated, a task that requires organizing, evaluating, analysing and unambiguously communicating the significance of data. In this context, working with ecological data, reflecting the complexities and interactions of the natural world, can be a challenge. Recent innovations for statistical analysis of multifaceted interrelated data make obtaining more accurate and meaningful results possible, but key decisions of the analyses to use, and which components to present in a scientific paper or report, may be overwhelming.
We offer a 10‐step protocol to streamline analysis of data that will enhance understanding of the data, the statistical models and the results, and optimize communication with the reader with respect to both the procedure and the outcomes. The protocol takes the investigator from study design and organization of data (formulating relevant questions, visualizing data collection, data exploration, identifying dependency), through conducting analysis (presenting, fitting and validating the model) and presenting output (numerically and visually), to extending the model via simulation. Each step includes procedures to clarify aspects of the data that affect statistical analysis, as well as guidelines for written presentation. Steps are illustrated with examples using data from the literature.
Following this protocol will reduce the organization, analysis and presentation of what may be an overwhelming information avalanche into sequential and, more to the point, manageable, steps. It provides guidelines for selecting optimal statistical tools to assess data relevance and significance, for choosing aspects of the analysis to include in a published report and for clearly communicating information.
Hierarchical models (HMs) represent a sequence of probability models for dependent random variables, of which one is typically observed (this is the data), and one or more random variables are unobserved and thus latent. Typical examples of the latter are the occurrence or abundance state of a local population. To describe patterns in each random variable, we typically use linear models, and for parameters representing rates or probabilities, we use link transformations just like in generalized linear models (GLMs). To fully exploit the power of HMs, you therefore need to have a good practical understanding of both linear models and GLMs. This chapter provides an applied introduction to both topics, which are essential in all of applied statistical modeling. HMs naturally contain random effects—i.e., sets of parameters assumed to be drawn from some statistical distribution—hence, HMs can also be called mixed models. Mixed models are a confusing topic to many ecologists, and hence we briefly review random-effects, or mixed, models as well. Linear models express the effects of covariates on a response as a simple sum. The covariates may be continuous, as in a regression model, or categorical (factors), as in analysis of variance (ANOVA). Linear models are an extremely powerful and yet simple way to describe patterns in the expected response from some stochastic system—i.e., the thing we want to describe and understand with our statistical model. The combination of different covariates or factors, interactions, and polynomial terms of continuous covariates offer tremendous power to describe such patterns. GLMs extend the principle of linear models to responses that need not be normal, but may be Poisson, Bernoulli, or binomial, and hence to the modeling of counts, proportions, or probabilities. Random effects are sets of parameters, or latent variables, that are described as the outcome of a stochastic system, and hence are given a probability distribution in the statistical model. They are typically invoked for a variety of reasons that include the following: extending the scope of inference of an analysis, quantifying and partitioning variability in one set of parameters, quantifying and modeling covariability (correlations) between two or more sets of parameters, modeling hidden structure in the data, borrowing strength, and combining information from different studies or data sets. We give an example of a normal-normal mixed model and a Poisson-normal generalized linear mixed model (GLMM). This chapter is meant to provide an applied review of linear models, GLMs, and random effects regardless of the inference paradigm (frequentist or Bayesian), and we give R and BUGS code to fit all the models.
Estimating survival and recruitment over large spatial scales is extremely challenging because it requires ‘expensive’ demographic data from marked animals. Dynamic N ‐mixture models (Dail & Madsen ) yield estimates of abundance, apparent survival, recruitment and detection probability from simple counts replicated in space and time, as frequently collected in monitoring programmes all over the world. However, the vital rates are assumed to be density‐ in dependent and constant over time, arguably unrealistic assumptions in many situations.
We evaluated the performance of the traditional dynamic N ‐mixture model from simulated data generated with density dependence and environmental stochasticity in the vital rates. We then developed a series of more advanced models that take density‐dependent vital rates and environmental stochasticity into account and assessed whether these models provide accurate estimates of vital rates, strength of density dependence and levels of environmental stochasticity.
We found that the traditional Dail–Madsen model produced accurate estimates of abundances and vital rates when the assumptions were met, but not when vital rates were subject to density dependence and environmental stochasticity.
Accurate parameter estimates were generally obtained when the data generation matched the data analysis model, but accuracy was substantially reduced otherwise. Interestingly, accurate estimates of abundance and detection were obtained from all models, regardless of the data generation model. Shorter time series from more sites yielded estimates of similar accuracy as longer series from fewer sites.
The new dynamic N ‐mixture models represent a promising basis for developing integrated population models that combine data from different sources to get insights into population dynamics of species over large areas.
Estimating the abundance or density of animal populations is often a fundamental task in ecological research and species conservation. N ‐mixture models are widely used to estimate the detection probability of individual organisms that thusly leads to more accurate estimates of a species' true abundance. However, individuals likely vary in their probabilities of being detected. During a survey, heterogeneity (variation) in individual detection probability might arise due to conditions of the surveying process; this form of extrinsic heterogeneity can be accounted for by the use of appropriate covariates in the models. In contrast, intrinsic heterogeneity in the detection probabilities of individuals arises when intraspecific variation in behaviour results in individual organisms differing in their latent (inherent) probabilities of being detected. This form of heterogeneity is not tractable by the use of covariates and its possible effects on model performance have not been investigated to date.
Using simulated data, we evaluated the performance of Poisson, negative binomial and zero‐inflated Poisson versions of N ‐mixture models under the conditions of intrinsic heterogeneity in individual detection probability.
Most versions of N ‐mixture models performed well in estimating abundance as indicated by relatively low root‐mean‐square‐error values ( RMSE < 1). Error distributions indicated a lack of substantial bias and relatively high precision and accuracy when simulated detection probabilities of individuals were high (>0·5) and heterogeneity was random. Otherwise, with structured heterogeneity (particularly positive density dependence) and low detection probabilities (<0·5), model performance was reduced ( RMSE > 2). The poorest performing model was the zero‐inflated Poisson version of N ‐mixture model applied to data from low survey effort.
Our results suggest that N ‐mixture models are robust to intrinsic heterogeneity in individual detection probabilities except when the detection probabilities are low. When model‐estimated detection probabilities are low (<0·5), model users should be aware that estimates of abundance could be erroneous if there was non‐random intrinsic heterogeneity in individual detection probabilities during the surveys. Remedying this situation might require redesigning the basic survey protocol such that it does not rely on behavioural traits (as cues to detection) that are intrinsically variable among individuals.
Count data of animals observed from multiple sites are commonly used to study variation in abundance across space and time. Because some individuals typically go undetected in such surveys, count data alone have traditionally been thought to not contain information about absolute abundance. In a recent paper, we showed that estimates of absolute abundance using single‐visit methods with covariates (Sólymos, et al . Environmetrics, 2012, 23, 197 ) can be biased arbitrarily low if the link function used for detection probability is mis‐specified. Sólymos & Lele (Methods in Ecology and Evolution, 2015, in press) argue that this is not a relevant issue in practice.
We discuss the implications of the assumptions necessary for estimating abundance from the single‐visit model and clarify and extend results in Knape & Korner‐Nievergelt (Methods in Ecology and Evolution, 2015, 6, 298). We also discuss assumptions of the Dail‐Madsen model.
We show that for the single‐visit model a partially scaled link function which covers the full range from 0 to 1 leads to the same scaling issue as the scaled link function used in Knape & Korner‐Nievergelt (2015) which only covers a restricted range.
We argue that there is essentially no information about absolute abundance contained in single‐visit count data. Additional more direct data on detection probabilities is required to robustly estimate absolute abundances.
Models of population dynamics are frequently used for purposes such as testing hypotheses about density dependence and predicting species' responses to future environmental change or conservation actions. Fitting models of population dynamics to field data is challenging because most data sets are characterized by observation error, which can inflate estimates of process variation if ignored. Recently, state-space models have been developed to deal with this problem by directly modeling both the observation error and the ecological process of interest. Conventional state-space models, however, have several important limitations: (1) they assume that random effects are Gaussian distributed, which implies that abundance can be negative and that false positive observation errors are equally likely as false negative errors; (2) they do not admit spatial variation in population dynamics; and (3) some of the parameters of the model are not estimable. We demonstrate how each of these problems can be resolved using a class of hierarchical models proposed by Dail and Madsen (2011) that attributes observation error to imperfect detection. We expand this class of models to accommodate classical growth models (e.g., exponential and Ricker-logistic), zero-inflation, and random effects. We also present methods for forecasting population size under future environmental conditions. Implementation of these ideas is possible using either frequentist or Bayesian methods, as demonstrated by accompanying R and JAGS code. Results of a simulation study suggest that bias is negligible and coverage nominal in most cases for the proposed model extensions. An analysis of data from the North American Breeding Bird Survey highlights how these methods can be readily applied to existing data, but it also suggests that precision will be low when direct information about detection probability (such as is collected using distance sampling or replicated counts) is lacking.
Insects' oviposition responses to resource and larval densities can be important factors determining distributions and competitive interactions of larvae. Aedes albopictus (Skuse) and Aedes aegypti (L.) (Diptera: Culicidae) show aggregated distributions of larvae in the field, larval interactions that are affected by detritus resources, and oviposition responses to resource and density cues in the laboratory. In this study, field experiments were conducted to test whether these species choose oviposition sites in response to chemical cues indicating detritus resource quantity and quality or larval abundances.In Experiment 1, both species showed interactive responses to water conditioned with high or low quantities of senescent live oak leaves and density combinations of A. albopictus and A. aegypti larvae. Aedes aegypti preferred high-detritus containers when conspecifics were absent. Aedes albopictus tended to prefer high-detritus containers when larval density was low. We found no evidence of interspecific differences in oviposition preferences.In Experiment 2, A. albopictus preferred high detritus to low or no detritus, and rapidly decaying, high-quality detritus to low-quality detritus.Oviposition choices by these Aedes are mainly determined by resource quantity and quality, with larval densities having minor, variable effects. Oviposition responses of these species are unlikely to lead to resource partitioning. Aggregated distributions of these species in the field are unlikely to be products of oviposition choices based on larval densities.
1. Binomial mixture models use repeated count data to estimate abundance. They are becoming increasingly popular because they provide a simple and cost-effective way to account for imperfect detection. However, these models assume that individuals are detected independently of each other. This assumption may often be violated in the field. For instance, manatees (Trichechus manatus latirostris) may surface in turbid water (i.e. become available for detection during aerial surveys) in a correlated manner (i.e. in groups). However, correlated behaviour, affecting the non-independence of individual detections, may also be relevant in other systems (e.g. correlated patterns of singing in birds and amphibians).
Summary1. Linear regression models are an important statistical tool in evolutionary and ecological studies. Unfortunately, these models often yield some uninterpretable estimates and hypothesis tests, especially when models contain interactions or polynomial terms. Furthermore, the standard errors for treatment groups, although often of interest for including in a publication, are not directly available in a standard linear model.2. Centring and standardization of input variables are simple means to improve the interpretability of regression coefficients. Further, refitting the model with a slightly modified model structure allows extracting the appropriate standard errors for treatment groups directly from the model.3. Centring will make main effects biologically interpretable even when involved in interactions and thus avoids the potential misinterpretation of main effects. This also applies to the estimation of linear effects in the presence of polynomials. Categorical input variables can also be centred and this sometimes assists interpretation.4. Standardization (z-transformation) of input variables results in the estimation of standardized slopes or standardized partial regression coefficients. Standardized slopes are comparable in magnitude within models as well as between studies. They have some advantages over partial correlation coefficients and are often the more interesting standardized effect size.5. The thoughtful removal of intercepts or main effects allows extracting treatment means or treatment slopes and their appropriate standard errors directly from a linear model. This provides a simple alternative to the more complicated calculation of standard errors from contrasts and main effects.6. The simple methods presented here put the focus on parameter estimation (point estimates as well as confidence intervals) rather than on significance thresholds. They allow fitting complex, but meaningful models that can be concisely presented and interpreted. The presented methods can also be applied to generalised linear models (GLM) and linear mixed models.
1. Trends of animal populations are of great interest in ecology but cannot be directly observed owing to imperfect detection. Binomial mixture models use replicated counts to estimate abundance, corrected for detection, in demographically closed populations. Here, we extend these models to open populations and illustrate them using sand lizard Lacerta agilis counts from the national Dutch reptile monitoring scheme.
2. Our model requires replicated counts from multiple sites in each of several periods, within which population closure is assumed. Counts are described by a hierarchical generalized linear model, where the state model deals with spatio-temporal patterns in true abundance and the observation model with imperfect counts, given that true state. We used WinBUGS to fit the model to lizard counts from 208 transects with 1–10 (mean 3) replicate surveys during each spring 1994–2005.
3. Our state model for abundance contained two independent log-linear Poisson regressions on year for coastal and inland sites, and random site effects to account for unexplained heterogeneity. The observation model for detection of an individual lizard contained effects of region, survey date, temperature, observer experience and random survey effects.
4. Lizard populations increased in both regions but more steeply on the coast. Detectability increased over the first few years of the study, was greater on the coast and for the most experienced observers, and highest around 1 June. Interestingly, the population increase inland was not detectable when the observed counts were analysed without account of detectability. The proportional increase between 1994 and 2005 in total lizard abundance across all sites was estimated at 86% (95% CRI 35–151).
5.Synthesis and applications. Open-population binomial mixture models are attractive for studying true population dynamics while explicitly accounting for the observation process, i.e. imperfect detection. We emphasize the important conceptual benefit provided by temporal replicate observations in terms of the interpretability of animal counts.
We report the results of three mark-release-recapture experiments carried out in an urban area in Rome, Italy, to study the active dispersal of Aedes albopictus (Diptera: Culicidae). The 4.3% recapture rate obtained supports the use of sticky traps in MRR experiments to study the dispersal of Ae. albopictus females. Most fluorescent dust-marked females were recaptured at the gravid stage at 50-200 m from the release sites during the first 9 days after release. The average of daily-MDTs (Mean Distance Traveled) was 119 m and the maximum observed distance travelled ranged from 199 m to 290 m in the three replicates. These data provide the first information about the dispersal of Ae. albopictus in a temperate European area and appear to be consistent with the few data available on this subject from other urban areas, where dispersal was constrained by physical barriers. Although caution should be taken in generalizing these results, they should be considered when planning control activities in urban areas in Italy, as well as in other European countries. This is particularly relevant if control is intended to interrupt pathogen transmission in cases of possible arbovirus epidemics, such as the Chikungunya outbreak that occurred in Ravenna, Italy in 2007.
The biology of Aedes albopictus is reviewed, with emphasis on studies of ecology and behavior. The following topics are discussed: distribution and taxonomy, genetics, medical importance, habitat, egg biology, larval biology, adult biology, competitive interactions, comparative studies with Aedes aegypti, population dynamics, photoperiodism, and surveillance and control.
The mosquito Aedes (Stegomyia) albopictus (Skuse) (Diptera: Culicidae), originally indigenous to South-east Asia, islands of the Western Pacific and Indian Ocean, has spread during recent decades to Africa, the mid-east, Europe and the Americas (north and south) after extending its range eastwards across Pacific islands during the early 20th century. The majority of introductions are apparently due to transportation of dormant eggs in tyres. Among public health authorities in the newly infested countries and those threatened with the introduction, there has been much concern that Ae. albopictus would lead to serious outbreaks of arbovirus diseases (Ae. albopictus is a competent vector for at least 22 arboviruses), notably dengue (all four serotypes) more commonly transmitted by Aedes (Stegomyia) aegypti (L.). Results of many laboratory studies have shown that many arboviruses are readily transmitted by Ae. albopictus to laboratory animals and birds, and have frequently been isolated from wild-caught mosquitoes of this species, particularly in the Americas. As Ae. albopictus continues to spread, displacing Ae. aegypti in some areas, and is anthropophilic throughout its range, it is important to review the literature and attempt to predict whether the medical risks are as great as have been expressed in scientific journals and the popular press. Examination of the extensive literature indicates that Ae. albopictus probably serves as a maintenance vector of dengue in rural areas of dengue-endemic countries of South-east Asia and Pacific islands. Also Ae. albopictus transmits dog heartworm Dirofilaria immitis (Leidy) (Spirurida: Onchocercidae) in South-east Asia, south-eastern U.S.A. and both D. immitis and Dirofilaria repens (Raillet & Henry) in Italy. Despite the frequent isolation of dengue viruses from wild-caught mosquitoes, there is no evidence that Ae. albopictus is an important urban vector of dengue, except in a limited number of countries where Ae. aegypti is absent, i.e. parts of China, the Seychelles, historically in Japan and most recently in Hawaii. Further research is needed on the dynamics of the interaction between Ae. albopictus and other Stegomyia species. Surveillance must also be maintained on the vectorial role of Ae. albopictus in countries endemic for dengue and other arboviruses (e.g. Chikungunya, EEE, Ross River, WNV, LaCrosse and other California group viruses), for which it would be competent and ecologically suited to serve as a bridge vector.
Collection methods currently used for large-scale sampling of adult Stegomyia mosquitoes (Diptera: Culicidae) present several operational limitations, which constitute major drawbacks to the epidemiological surveillance of arboviruses, the evaluation of the impact of control strategies, and the surveillance of the spreading of allochthonous species into non-endemic regions. Here, we describe a new sticky trap designed to capture adult container-breeding mosquitoes and to monitor their population dynamics. We tested the sampling properties of the sticky trap in Rome, Italy, where Aedes (Stegomyia) albopictus is common. The results of our observations, and the comparison between sticky trap catches and catches made with the standard oviposition trap, are presented. The sticky trap collected significantly larger numbers of Ae. albopictus females than any other Culicidae species representing >90% of the total catches. A maximum of 83 An. albopictus females was collected in a single week. A high correlation (Pearson correlation coefficient r= 0.96) was found between the number of females and the number of eggs collected by the traps. The functional relationship between the number of eggs and the number of adult females was assessed by major axis regression fitted to log(1 +x)-transformed trap counts as y= 0.065 + 1.695x. Trap samples significantly departed from a random distribution; Taylor's power law was fitted to the trap samples to quantify the degree of aggregation in the catches, returning the equations s(2)= 2.401 m(1.325) for the sticky trap and s(2)= 13.068 m(1.441) for the ovitrap, with s(2) and m denoting the weekly catch variance and mean, respectively, indicating that eggs were significantly more aggregated than mosquitoes (P < 0.0001). Taylor's power law parameters were used to estimate the minimum number of sample units necessary to obtain sample estimates with a fixed degree of precision and sensitivity. For the range of densities encountered in our study area during the Ae. albopictus breeding season, the sticky trap was more precise and sensitive than the ovitrap. At low population densities (c. < 0.1 mosquito/trap), however, the ovitrap was more sensitive at detecting the presence of this species. Overall, our results indicate that our new model of sticky trap can be used to sample Ae. albopictus females in urban environments, and, possibly, other container-breeding Stegomyia mosquitoes (e.g. Aedes aegypti). The technical properties of the new trap are discussed with respect to its possible application in monitoring the population dynamics of container-breeding mosquitoes, in studying their bionomics, and in vector surveillance and, possibly, control.
Chikungunya virus (CHIKV), which is transmitted by Aedes spp mosquitoes, has recently caused several outbreaks on islands in the Indian Ocean and on the Indian subcontinent. We report on an outbreak in Italy.
After reports of a large number of cases of febrile illness of unknown origin in two contiguous villages in northeastern Italy, an outbreak investigation was done to identify the primary source of infection and modes of transmission. An active surveillance system was also implemented. The clinical case definition was presentation with fever and joint pain. Blood samples were gathered and analysed by PCR and serological assays to identify the causal agent. Locally captured mosquitoes were also tested by PCR. Phylogenetic analysis of the CHIKV E1 region was done.
Analysis of samples from human beings and from mosquitoes showed that the outbreak was caused by CHIKV. We identified 205 cases of infection with CHIKV between July 4 and Sept 27, 2007. The presumed index case was a man from India who developed symptoms while visiting relatives in one of the villages. Phylogenetic analysis showed a high similarity between the strains found in Italy and those identified during an earlier outbreak on islands in the Indian Ocean. The disease was fairly mild in nearly all cases, with only one reported death.
This outbreak of CHIKV disease in a non-tropical area was to some extent unexpected and emphasises the need for preparedness and response to emerging infectious threats in the era of globalisation.
Italia: focolai autoctoni di infezione da virus chikungunya
- Iss
- Istituto Superiore Di Sanità
ISS. Istituto Superiore di Sanità. (2017). Italia: focolai autoctoni di infezione da virus chikungunya.
[Italy: autochthonous cases of chikungunya virus]. Rome: 21 Dec 2017. Italian. Available from:
http://www.salute.gov.it/portale/temi/documenti/chikungunya/bollettino_chikungunya_ULTIMO.pdf
Cited 8 August 2018
JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling
- M Plummer
Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs
sampling. Proceedings of the 3rd International Workshop on Distributed Statistical Computing, (Dsc),
1-10. doi:ISSN 1609-395X
Fact sheet: Vector-borne diseases
- Who. World Health Organization
WHO. World Health Organization. Fact sheet: Vector-borne diseases. 31 October 2017. Available
from: http://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases Cited 8 August 2018.