Accounting for weather and time-of-day parameters when analysing count data from monitoring programs

Problems induced by heterogeneity in species and individuals detectability are now well recognized when analysing count data. Yet, most recent techniques developed to handle this problem are still hardly applicable to many monitoring schemes, and do not provide abundance estimates at the point count scale. Here, we show how using simple weather variables can be a useful surrogate to detect variability in species detectability. We further look for a potential bias or loss in statistical power based on count data while ignoring weather and time-of-day variables. We first used the French Breeding Bird Survey to test how each of the counts of the 97 most common breeding species was influenced by weather and time-of-day variables. We assessed how the estimation of each species response to fragmentation could be influenced by correcting counts with such variables. Among 97 species, 75 were affected by at least one of the five weather and time-of-day variables considered. Despite these strong influences, the relationship between species abundance and fragmentation was not biased when not controlling counts for weather and time-of-day variables and further found no improvement in statistical power when accounting for these variables. Our results show that simple variables can be very powerful to assess how species detectability is influenced by weather conditions but they are inconsistent with any specific bias due to heterogeneous detectability. We suggest that raw count data can be used without any correction in case the sources of variation in detectability could be considered independent to the factor of interest.     

Effects of experimental playbacks on availability for detection during point counts

Point counts are the most commonly used technique for surveying passerines during the breeding season. Several methods for estimating probabilities of detection during point count surveys have been developed. These methods have focused primarily on accounting for the influence of environmental factors (e.g., weather and noise) on detectability, however, the probability that birds are available for detection (e.g., sings or moves) during point counts has received less attention. We used sequential point counts to determine the effect of playback of the mobbing calls of Black‐capped Chickadees (Poecile atricapillus) and the flight calls of Red‐tailed Hawks (Buteo jamaicensis) on availability for detection (e.g., singing or moving) during point‐count surveys. We conducted 180 point counts over a 2‐yr period in central – east central Minnesota to evaluate the possible effect of playbacks on observed density, overall species richness, minute of first detection, and distance of first detection. We also used removal models to quantify the magnitude of changes in detectability and direction of response to playbacks for 10 focal species. Playback of the mobbing calls of Black‐capped Chickadees increased observed density and decreased the average distance of detection and time of first detection, whereas playback of the flight calls of a Red‐tailed Hawk resulted in a decrease in observed density and species richness, and an increased time of first detection. Playback treatment was a covariate in all best performing models for the 10 species analyzed, but the magnitude and direction of response to playbacks were species specific. The importance of playback type in detectability models indicates that the calls of heterospecifics can influence species availability for detection. As such, researchers using playback methods should seek to quantify species‐specific responses in detection probability and consider how component detection probabilities could influence survey outcomes.     

Estimating Abundances of Interacting Species Using Morphological Traits,Foraging Guilds,and Habitat

We developed a statistical model to estimate the abundances of potentially interacting species encountered while conducting point-count surveys at a set of ecologically relevant locations – as in a metacommunity of species. In the model we assume that abundances of species with similar traits (e.g., body size) are potentially correlated and that these correlations, when present, may exist among all species or only among functionally related species (such as members of the same foraging guild). We also assume that species-specific abundances vary among locations owing to systematic and stochastic sources of heterogeneity. For example, if abundances differ among locations due to differences in habitat, then measures of habitat may be included in the model as covariates. Naturally, the quantitative effects of these covariates are assumed to differ among species. Our model also accounts for the effects of detectability on the observed counts of each species. This aspect of the model is especially important for rare or uncommon species that may be difficult to detect in community-level surveys. Estimating the detectability of each species requires sampling locations to be surveyed repeatedly using different observers or different visits of a single observer. As an illustration, we fitted models to species-specific counts of birds obtained while sampling an avian community during the breeding season. In the analysis we examined whether species abundances appeared to be correlated due to similarities in morphological measures (body mass, beak length, tarsus length, wing length, tail length) and whether these correlations existed among all species or only among species of the same foraging guild. We also used the model to estimate the effects of forested area on species abundances and the effects of sound power output (as measured by body size) on species detection probabilities.     

Water conditions influence the detectability of Crossodactylus gaudichaudii (Anura,Hylodidae) in streams of the Atlantic Forest

Stream-dwelling amphibians' occurrence, behaviour and reproductive success are strongly influenced by dynamic abiotic factors, for example, water flow and spray. These factors can disproportionately affect these frogs due to their dependency on specific favourable conditions for development and incapacity to disperse from unfavourable environments. We analysed the influence of environmental covariates on the detectability of Crossodactylus gaudichaudii, an amphibian species endemic to streams in the Brazilian Atlantic Forest. We conducted sampling in the streams of the Duas Bocas Biological Reserve, Brazil, and we measured air and water temperature, air humidity and pH of water on each sampling occasion. We estimated the effects of variables on the detectability of the species using single-season occupancy models. Our results indicated that the detectability of the species increases on occasions with higher water temperatures (24–26°C) and lower pH (5.0–5.5). We investigated the influence of these covariates only on the detectability of adult frogs, but it is likely that the physiochemical properties of stream water are more important to aquatic larvae, and hence, the probability of their detection. Given this, further studies should examine the relevance of covariates on the detectability of adult frogs as well as larvae.     

Species richness estimation and determinants of species detectability in butterfly monitoring programmes

Abstract 1. Species richness is the most widely used biodiversity index, but can be hard to measure. Many species remain undetected, hence raw species counts will often underestimate true species richness. In contrast, capture–recapture methods estimate true species richness and correct for imperfect and varying detectability. 2. Detectability is a crucial quantity that provides the link between a species count and true species richness. For insects, it has hardly ever been estimated, although this is required for the interpretation of species counts. 3. In the Swiss butterfly monitoring programme about 100 transect routes are surveyed seven times a year using a highly standardised protocol. In July 2003, control observers made two additional surveys on 38 transects. Data from these 38 quadrats were analysed to see whether currently available capture–recapture models can provide quadrat‐specific estimates of species richness, and to estimate species detectability in relation to transect, observer, survey, region, and abundance. 4. Species richness over the entire season cannot be estimated using current capture–recapture methods. The species pool was open, preventing use of closed population models, and detectability varied by species, preventing use of current open population models. Assuming a closed species pool during two mid‐season (July) surveys, a Jackknife capture–recapture method was used that accounts for heterogeneity to estimate mean detectability and species richness. 5. In every case, more species were present than were counted. Mean species detectability was 0.61 (SE 0.01) with significant differences between observers (range 0.37–0.83). Species‐specific detection at time t+ 1 was then modelled for those species seen at t for three mid‐season surveys. Detectability averaged 0.50 (range 0.17–0.81) for individual species and 0.65, 0.44, and 0.42 for surveys. Abundant species were detected more easily, although this relationship explained only 5% of variation in species detectability. 6. These are important, although not entirely unexpected, results for species richness estimation of short‐lived animals. Raw counts of species may be misleading species richness indicators unless many surveys are conducted. Monitoring programmes should be calibrated, i.e. the assumption of constant detectability over dimensions of interest needs to be tested. The development of capture–recapture or similar models that can cope with both open populations and heterogeneous species detectability to estimate species richness should be a research priority.     

Patterns of habitat use and selection by the capybara (Hydrochoerus hydrochaeris): a landscape-scale analysis

Habitat selection analysis provides useful information on how animals become distributed in response to spatial heterogeneity. Here, we analyze the habitat use and selection of different water bodies (marsh, swamps and round shallow lakes) by capybaras (Hydrochoerus hydrochaeris) and their relation to environmental variables during contrasting climate-hydrological seasons in the Esteros del Iberá (Corrientes, Argentina). We evaluated the intensity of use by capybaras through the total number of individuals (abundance) in each water body, the number and mean size of social groups, and the physical and vegetation characteristics of the environment. The capybaras used marsh and swamps according to availability in both seasons, while they used rounded shallow lakes less than their available would suggest in summer. The use intensity of different rounded shallow lakes estimated based on group size did not show significant differences. In contrast, significant differences were observed when evaluated by the number of individuals in each rounded shallow lake. Different intensity of use was closely associated with environmental and vegetation characteristics. The results show that habitat suitability for capybaras is associated with vegetation cover and “embalsados” in the low-lying area rather than with the morphometry of the rounded shallow lakes. The pattern of habitat selection may depend on forage quality, water availability for thermoregulation and mating, and presence of shelter and resting sites. On the other hand, the present study shows how the size, shape and the abundance of different types of water bodies affect population abundance and density.     

Use of log-linear analysis to construct explanatory models for TDBP- and AFB1-induced mutation spectra in lacI transgenic animals

Mutation spectra recovered from lacI transgenic animals exposed in separate experiments to tris-(2,3-dibromopropyl)phosphate (TDBP) or aflatoxin B1 (AFB1) were examined using log-linear analysis. Log-linear analysis is a categorical procedure that analyses contingency table data. Expected contingency table cell counts are estimated by maximum likelihood as effects of main variables and variable interactions. Evaluation of hierarchical models of decreasing complexity indicates when significant explanatory power is lost by the sequential omission of interactions between variables. Use of this technique allows construction of the most parsimonious models to account for mutation spectra obtained in the two experiments. The resulting statistical models are consistent with previous analyses of these data and with biological explanations for causes of the observed spectra.     

Effect of species rarity on the accuracy of species distribution models for reptiles and amphibians in southern California

Aim Several studies have found that more accurate predictive models of species’ occurrences can be developed for rarer species; however, one recent study found the relationship between range size and model performance to be an artefact of sample prevalence, that is, the proportion of presence versus absence observations in the data used to train the model. We examined the effect of model type, species rarity class, species’ survey frequency, detectability and manipulated sample prevalence on the accuracy of distribution models developed for 30 reptile and amphibian species. Location Coastal southern California, USA. Methods Classification trees, generalized additive models and generalized linear models were developed using species presence and absence data from 420 locations. Model performance was measured using sensitivity, specificity and the area under the curve (AUC) of the receiver‐operating characteristic (ROC) plot based on twofold cross‐validation, or on bootstrapping. Predictors included climate, terrain, soil and vegetation variables. Species were assigned to rarity classes by experts. The data were sampled to generate subsets with varying ratios of presences and absences to test for the effect of sample prevalence. Join count statistics were used to characterize spatial dependence in the prediction errors. Results Species in classes with higher rarity were more accurately predicted than common species, and this effect was independent of sample prevalence. Although positive spatial autocorrelation remained in the prediction errors, it was weaker than was observed in the species occurrence data. The differences in accuracy among model types were slight. Main conclusions Using a variety of modelling methods, more accurate species distribution models were developed for rarer than for more common species. This was presumably because it is difficult to discriminate suitable from unsuitable habitat for habitat generalists, and not as an artefact of the effect of sample prevalence on model estimation.     

The prediction of macrophyte species occurrence in Swiss ponds

The study attempted to model the abundance of aquatic plant species recorded in a range of ponds in Switzerland. A stratified sample of 80 ponds, distributed all over the country, provided input data for model development. Of the 154 species recorded, 45 were selected for modelling. A total of 14 environmental parameters were preselected as candidate explanatory variables. Two types of statistical tools were used to explore the data and to develop the predictive models: linear regression (LR) and generalized additive models (GAMs). Six LR species models had a reasonable predictive ability (30–50% of variance explained by the selected predictors). There was a gradient in the quality of the 45 GAM models. Ten species models exhibited both a good fit and statistical robustness: Lemna minor, Phragmites australis, Lysimachia vulgaris, Galium palustre, Lysimachia nummularia, Iris pseudacorus, Lythrum salicaria, Lycopus europaeus, Phalaris arundinacea, Alisma plantago-aquatica, Schoenoplectus lacustris, Carex nigra. Altitude appeared to be a key explanatory variable in most of the species models. In some cases, the degree to which the shore was shaded, connectivity between water bodies, pond area, mineral nitrogen levels, pond age, pond depth, and the extent of agriculture or pasture in the catchment were selected as additional explanatory variables. The species models demonstrated that it is possible to predict species abundance of aquatic macrophytes and that each species responded individually to distinct environmental variables.     

Life cycle of Amblyomma cooperi (Acari: Ixodidae) using capybaras (Hydrochaeris hydrochaeris) as hosts

The life cycle of Amblyomma cooperi was evaluated under laboratory conditions testing different host species. Larval infestations were performed on chickens (Gallus gallus) and capybaras (Hydrochaeris hydrochaeris). Nymphal infestations were performed on G. gallus, H. hydrochaeris, guinea pigs (Cavia porcellus) and wild mice (Calomys callosus). Infestations by adult ticks were performed only on capybaras. All free-living stages were observed in darkness at 27 degrees C and RH 85%. Capybaras were significantly (p < 0.05) the most suitable hosts for immature ticks, with the highest larval (63.6%) and nymphal (48%) recovery. Larval and nymphal feeding and premolt periods were significantly different (p < 0.05) between ticks fed on different host species. Male nymphs showed premolt period significantly shorter (p < 0.05) than female nymphs. The overall sex ratio of adult ticks was 0.92:1 (M:F). Infestations by adult ticks on capybaras yielded more than 76% of engorged female recovery. Only three out of 33 engorged females fed on capybaras did not lay fertile eggs. The life cycle of A. cooperi in laboratory, reported for the first time, was completed in an average period of 189.4 days. During the premolt period, all A. cooperi engorged nymphs secreted distinct blackish drops, which seem to be inherent to this species. Our results, associated data in the literature, confirm the high suitability of capybaras for the adult stage of A. cooperi and also indicate this animal species as a primary host for immature stages of A. cooperi in nature. On the other hand, the results of larval and nymphal infestation on chickens and guinea pigs suggest that birds and wild guinea pigs, which are also present in the distribution area of A. cooperi in South America, could be potentially infested by A. cooperi immature stages in nature.     

Evaluation of three methods to estimate density and detectability from roadside point counts

Roadside point counts are often used to estimate trends of bird populations. The use of aural counts of birds without adjustment for detection probability, however, can lead to incorrect population trend estimates. We compared precision of estimates of density and detectability of whistling northern bobwhites (Colinus virginianus) using distance sampling, independent double-observer, and removal methods from roadside surveys. Two observers independently recorded each whistling bird heard, distance from the observer, and time of first detection at 362 call-count stops in Ohio. We examined models that included covariates for year and observer effects for each method and distance from observer effects for the double-observer and removal methods using Akaike's Information Criterion (AIC). The best model of detectability from distance sampling included observer and year effects. The best models from the removal and double-observer techniques included observer and distance effects. All 3 methods provided precise estimates of detection probability (CV = 2.4–4.4%) with a range of detectability of 0.44–0.95 for a 6-min survey. Density estimates from double-observer surveys had the lowest coefficient of variation (2005 = 3.2%, 2006 = 1.7%), but the removal method also provided precise estimates of density (2005 CV = 3.4%, 2006 CV = 4.8%), and density estimates from distance sampling were less precise (2005 CV = 9.6%, 2006 CV = 7.9%). Assumptions of distance sampling were violated in our study because probability of detecting bobwhites near the observer was <1 or the roadside survey points were not randomly distributed with respect to the birds. Distances also were not consistently recorded by individual members of observer pairs. Although double-observer surveys provided more precise estimates, we recommend using the removal method to estimate detectability and abundance of bobwhites. The removal method provided precise estimates of density and detection probability and requires half the personnel time as double-observer surveys. Furthermore, the likelihood of meeting model assumptions is higher for the removal survey than with independent double-observers. © 2011 The Wildlife Society.     

Intestinal helminths of capybaras, Hydrochoerus hydrochaeris, from Venezuela

Quantitative parameters of intestinal helminth species and their potential relations to host characteristics in a population of capybaras (Hydrochoerus hydrochaeris) from Venezuela are reported for the first time. The intestines of 40 capybaras were collected during the 1992-annual harvest at Hato El Cedral. Six helminth species were found: 2 cestodes (Monoecocestus macrobursatum, M. hagmanni), 2 nematodes (Viannella hydrochoeri, Protozoophaga obesa), and 2 trematodes (Hippocrepis hippocrepis, Taxorchis schistocotyle). This is the first report for M. macrobursatum in Venezuela. Helminth abundance did not differ between sexes or age classes. Although patterns of distribution for all helminth species were overdispersed, the high prevalence found for all species (over 70%) and the high abundance observed for nematodes made it difficult to assess the effect that these helminths may produce on capybaras. Nevertheless, the negative associations found between the body condition of capybaras and helminth intensity for M. macrobursatum and V. hydrochoeri, might be pointing out potential host population regulatory role for these parasites which require further research.     

Effect of vegetation type and environmental factors on European wild rabbitOryctolagus cuniculus counts in a southern Portuguese montado

This study assesses the effect of vegetation and variables related to weather and light conditions on the efficacy of rabbitOryctolagus cuniculus (Linnaeus, 1758) counts carried out in the south of Portugal. Counts were carried out in two years using driven line transects, and correlated with vegetation type and the variables using generalised linear models. The offset was a surveyed area estimated using Distance Sampling Theory as a means of correcting for detectability bias. More rabbits were observed in dense vegetation during day-time counts and in crops during night-time counts. In 1998, day-time counts were higher with higher average daily temperatures, whilst the night-time counts were higher with higher minimum daily temperatures. In 1999, day-time counts decreased with the amount of rainfall in the previous month, and the night-time counts decreased with the accumulated rainfall in the previous two months and with the higher wind speeds. In order to increase efficacy, counts should be carried out either at dawn or at dusk during the post-breeding season, and with greater intensity in dense scrub or open vegetation with high tree cover. During the breeding season and winter, counts should be carried out after dusk and with greater intensity in arable crops.     

Spatial variation in insect community and species responses to habitat loss and plant community composition

Several experimental studies have examined species responses to manipulations of habitat area and spatial arrangement, but plant composition and spatial variation in species distributions also affect animal responses to habitat alteration. We used an experimental approach to study the combined effects of habitat area, edge, and plant community composition on the spatial structure of insect species richness and composition. The abundance of three guilds (herbivores, predators and parasitoids) and individual species were also analyzed. Habitat patches were created that differed in area and edge by selectively mowing portions of 15 m×15 m plots in a 1.7-ha old field. Spatial and environmental variables were used to predict insect responses in separate multiple regression and ordination models. The variation in species responses due to spatial and environmental variables was then partitioned by combining these variables into an overall regression or ordination. Spatial and environmental variables contributed similar percentages to the total variance in insect species richness, abundance or composition. No significant effects of habitat area were observed in any response variable. Herbivore abundance showed positive responses to legume or grass cover, as well as spatial variation that was unrelated to environmental variables. Predators and parasitoids had greater effects of plant species richness and habitat edge, and less unexplained spatial variation. Individual species differed in their responses to plant variables, depending on host specialization or intraspecific aggregation. Our study highlights the importance of plant community composition and spatial variation apart from environmental variables. Spatial variation stems both from species responses to environmental features as well as species differences in habitat specialization and intraspecific aggregation.     

黄土残塬沟壑区流域次生植被物种分布的地形响应

研究流域次生植被物种对地形因子的响应规律,识别影响次生植被物种分布的主要地形因子,是流域近自然植被生态恢复和重建的基础。采用ArcGIS空间分析模块和地形分析模块TauDEM,并与统计软件SPLUS2000中的GRASP工具相结合,建立了位于黄土高原残垣沟壑区山西省吉县蔡家川流域次生植被各个物种分布基于地形因子的广义相加模型(GAM)。模型中的地形因子包括:海拔、坡向、坡度、平面曲率、坡位指数(SPI)、地形湿度指数(TWI)、单宽汇水面积(SCA)等。受试者操作特征曲线(ROC)测试中AUC值表明:大部分测试物种(约62%)拟合模型效果较好,且模型较为稳定。总体来看,研究流域次生植被物种分布体现了水分限制的空间分异特征:阴坡各物种分布概率较大,且随海拔升高而减小。影响研究流域次生植被物种空间分布的潜在重要因子为海拔和坡向,而单宽汇水面积(SCA)和地形湿度指数(TWI)虽然是多个物种响应模型的预测因子,但受高一级尺度海拔的影响,SCA与TWI对物种分布的影响作用较小;坡度影响作用最小。据此,在流域植被恢复和防护林建设目标区选择及立地条件划分时应首先以海拔和坡向为依据,单宽汇水面积(SCA)和地形湿度指数(TWI)则可以作为次一级立地分类依据,而坡度则仅能作为最后一级的分类依据。     

The influence of environmental variables on the density of larval lampreys in different seasons

Summary The objective of the study was to identify a subset of a set of twenty environmental variables which could explain variations in the density of larval lampreys (Geotria australis) in a south-western Australian stream. Generalised linear modelling, assuming Poisson distributions for the larval counts, led to a different model for each of the four seasons, with variations in larval density being explained in each season by a combination of between five and eight environmental variables. The influence of stream region also had to be taken into account in the model for winter.Four environmental variables (substrate organic material and chlorophyll a, macrophyte roots and low-angle shading) were present in three of the four seasonal models. A further six variables (water depth, substrate depth and profile, medium-sized sands, light intensity, and the presence of an eddy) were each found useful for two models. Two variables (current velocity and substrate profile) were each retained in one model. Eight of the twenty variables were not required for any of the seasonal models. The importance of organic material, shade, eddies, current velocity substrate particle size and a sufficient depth of substrate in our models agree with the largely subjective assessments of larval lamprey habitats made in the field by many previous workers for other lamprey species in diverse geographical localities.Our finding that larval density increased with increases in organic material and unicellular algae in the substrate and with shade, contrasts with the results of a different model based on data collected in a northern European stream. These differences can be related to our use of a more rigorous and comprehensive sampling regime and a more appropriate form of statistical analysis.     

Aposematism and crypsis are not enough to explain dorsal polymorphism in the Iberian adder

Aposematic organisms can show phenotypic variability across their distributional ranges. The ecological advantages of this variability have been scarcely studied in vipers. We explored this issue in Vipera seoanei, a species that exhibits five geographically structured dorsal colour phenotypes across Northern Iberia: two zigzag patterned (Classic and Cantabrica), one dorsal-strip patterned (Bilineata), one even grey (Uniform), and one melanistic (Melanistic). We compared predation rates (raptors and mammals) on plasticine models resembling each colour phenotype in three localities. Visual modelling techniques were used to infer detectability (i.e. conspicuousness) of each model type for visually guided predators (i.e. diurnal raptors). We hypothesize that predation rates will be lower for the two zigzag models (aposematism hypothesis) and that models with higher detectability would show higher predation rates (detectability hypothesis). Classic and Bilineata models were the most conspicuous, while Cantabrica and Uniform were the less. Melanistic presented an intermediate conspicuousness. Predation rate was low (3.24% of models) although there was variation in attack frequency among models. Zigzag models were scarcely predated supporting the aposematic role of the zigzag pattern in European vipers to reduce predation (aposematism hypothesis). From the non-zigzag models, high predation occurred on Bilineata and Melanistic models, and low on Uniform models, partially supporting our detectability hypothesis. These results suggest particular evolutionary advantages for non-zigzag phenotypes such as better performance of Melanistic phenotypes in cold environments or better crypsis of Uniform phenotypes. Polymorphism in V. seoanei may respond to a complex number of forces acting differentially across an environmental gradient.     

Predicting species distributions: a critical comparison of the most common statistical models using artificial species

Aim To test statistical models used to predict species distributions under different shapes of occurrence–environment relationship. We addressed three questions: (1) Is there a statistical technique that has a consistently higher predictive ability than others for all kinds of relationships? (2) How does species prevalence influence the relative performance of models? (3) When an automated stepwise selection procedure is used, does it improve predictive modelling, and are the relevant variables being selected? Location We used environmental data from a real landscape, the state of California, and simulated species distributions within this landscape. Methods Eighteen artificial species were generated, which varied in their occurrence response to the environmental gradients considered (random, linear, Gaussian, threshold or mixed), in the interaction of those factors (no interaction vs. multiplicative), and on their prevalence (50% vs. 5%). The landscape was then randomly sampled with a large (n = 2000) or small (n = 150) sample size, and the predictive ability of each statistical approach was assessed by comparing the true and predicted distributions using five different indexes of performance (area under the receiver‐operator characteristic curve, Kappa, correlation between true and predictive probability of occurrence, sensitivity and specificity). We compared generalized additive models (GAM) with and without flexible degrees of freedom, logistic regressions (general linear models, GLM) with and without variable selection, classification trees, and the genetic algorithm for rule‐set production (GARP). Results Species with threshold and mixed responses, additive environmental effects, and high prevalence generated better predictions than did other species for all statistical models. In general, GAM outperforms all other strategies, although differences with GLM are usually not significant. The two variable‐selection strategies presented here did not discriminate successfully between truly causal factors and correlated environmental variables. Main conclusions Based on our analyses, we recommend the use of GAM or GLM over classification trees or GARP, and the specification of any suspected interaction terms between predictors. An expert‐based variable selection procedure was preferable to the automated procedures used here. Finally, for low‐prevalence species, variability in model performance is both very high and sample‐dependent. This suggests that distribution models for species with low prevalence can be improved through targeted sampling.     

Dealing with varying detection probability, unequal sample sizes and clumped distributions in count data

Temporal variation in the detectability of a species can bias estimates of relative abundance if not handled correctly. For example, when effort varies in space and/or time it becomes necessary to take variation in detectability into account when data are analyzed. We demonstrate the importance of incorporating seasonality into the analysis of data with unequal sample sizes due to lost traps at a particular density of a species. A case study of count data was simulated using a spring-active carabid beetle. Traps were 'lost' randomly during high beetle activity in high abundance sites and during low beetle activity in low abundance sites. Five different models were fitted to datasets with different levels of loss. If sample sizes were unequal and a seasonality variable was not included in models that assumed the number of individuals was log-normally distributed, the models severely under- or overestimated the true effect size. Results did not improve when seasonality and number of trapping days were included in these models as offset terms, but only performed well when the response variable was specified as following a negative binomial distribution. Finally, if seasonal variation of a species is unknown, which is often the case, seasonality can be added as a free factor, resulting in well-performing negative binomial models. Based on these results we recommend (a) add sampling effort (number of trapping days in our example) to the models as an offset term, (b) if precise information is available on seasonal variation in detectability of a study object, add seasonality to the models as an offset term; (c) if information on seasonal variation in detectability is inadequate, add seasonality as a free factor; and (d) specify the response variable of count data as following a negative binomial or over-dispersed Poisson distribution.     

Occupancy models including local and landscape variables are useful to assess the distribution of a salamander species at risk

Numerous amphibian species are at risk of extinction worldwide. Therefore, reliable estimations of the distribution and abundance of these species are necessary for their conservation. Generally, amphibians are difficult to detect in the wild, which compromises the accuracy of long-term population monitoring and management. Occupancy models are useful tools to assess how environmental variables, at a local and at a landscape scale, affect the distribution and abundance of organisms taking into account species imperfect detectability. In this study, we evaluated with an environmental multiscale approach the seasonal variation of the occupation area of the threatened salamander, Ambystoma ordinarium along its distribution range. We obtained readings in 60 streams of physicochemical variables associated with habitat quality and landscape features. We found that detection and occupation probability of A. ordinarium are seasonally associated with different environmental variables. During the dry season, detectability was positively associated with temperature and stream depth, whereas occupancy was positively associated with the proportion of crops in the landscape and stream elevation. In the rainy season, the detection probability was not explained by any variable considered, and occupancy was negatively associated with stream's electrical conductivity and dissolved oxygen. Based on the estimation of occupied sites, we showed that A. ordinarium presents a more restricted distribution range than previously projected. Therefore, our results reveal the importance of evaluating the accuracy of distribution estimates for the conservation of threatened species as A. ordinarium.