Aerial Surveys for Estimating Wild Turkey Abundance in the Texas Rolling Plains

Abstract: Aerial surveys have been used to estimate abundance of several wild bird species including wild turkeys (Meleagris gallopavo). We used inflatable turkey decoys at 3 study sites in the Texas Rolling Plains to simulate Rio Grande wild turkey (M. g. intermedia) flocks. We evaluated detectability of flocks and errors in counting flock size during fixed-wing (Cessna 172) aerial surveys using logistic and linear regression models. Flock detectability was primarily influenced by flock size and vegetative cover, and errors in counting flock size were primarily influenced by size of flocks. We conducted computer simulations to evaluate the accuracy and precision of fixed-wing aerial surveys and examined power to detect trends in population change. Our simulations suggested abundance estimates from fixed-wing aerial surveys may be underestimated by 10-15% (2.0-4.8% CV). Power analyses suggested that fixed-wing aerial surveys can provide sufficient power (>0.80) to detect a population change of 10-25% over a 4-5-year period. We concluded fixed-wing aerial surveys are feasible on ecoregion scales.     

Estimating Detection Probabilities of Waterfowl Broods From Ground-Based Surveys

ABSTRACT Brood:pair ratios could provide an economical method for assessing spatial or temporal variation in waterfowl productivity, but such estimators are severely biased by incomplete detection of broods. We conducted 3 sequential counts of 1,357 waterfowl broods in northeastern North Dakota, USA, and used closed-population mark-recapture models to estimate total brood abundance while controlling for variation in detection probabilities (p). Blue-winged teal (Anas discors) broods had the lowest average detection probability (p = 0.305), whereas diving-duck broods had the highest average detectability (p = 0.571). Detection was generally highest in morning or evening, but temporal patterns varied among species and there was no survey window that maximized detection probabilities for all species. Detection probabilities averaged 0.108 (SD = 0.056) higher for an experienced observer versus an inexperienced observer. Detection probabilities were 0.044 higher for roadside versus walk-up surveys and increased with increasing brood size, total brood abundance, survey date, wind speed, temperature, cloud cover, and amount of time spent surveying each wetland. Detection probabilities declined with increasing wetland size and amount of tall peripheral vegetation. Our mark-recapture results indicated that a traditional unreplicated brood survey would have missed 67.5% of estimated broods, summed over all species. Use of closed-population mark-recapture techniques provided an effective method for reducing this bias and identifying and quantifying factors that reduce detection probabilities of waterfowl broods. We recommend that future brood surveys incorporate 2 or 3 temporally segregated replicate counts to allow for formal estimation of detection probabilities.     

A Sightability Model for Mountain Goats

ABSTRACT Unbiased estimates of mountain goat (Oreamnos americanus) populations are key to meeting diverse harvest management and conservation objectives. We developed logistic regression models of factors influencing sightability of mountain goat groups during helicopter surveys throughout the Cascades and Olympic Ranges in western Washington during summers, 2004–2007. We conducted 205 trials of the ability of aerial survey crews to detect groups of mountain goats whose presence was known based on simultaneous direct observation from the ground (n = 84), Global Positioning System (GPS) telemetry (n = 115), or both (n = 6). Aerial survey crews detected 77% and 79% of all groups known to be present based on ground observers and GPS collars, respectively. The best models indicated that sightability of mountain goat groups was a function of the number of mountain goats in a group, presence of terrain obstruction, and extent of overstory vegetation. Aerial counts of mountain goats within groups did not differ greatly from known group sizes, indicating that under-counting bias within detected groups of mountain goats was small. We applied Horvitz-Thompson-like sightability adjustments to 1,139 groups of mountain goats observed in the Cascade and Olympic ranges, Washington, USA, from 2004 to 2007. Estimated mean sightability of individual animals was 85% but ranged 0.75–0.91 in areas with low and high sightability, respectively. Simulations of mountain goat surveys indicated that precision of population estimates adjusted for sightability biases increased with population size and number of replicate surveys, providing general guidance for the design of future surveys. Because survey conditions, group sizes, and habitat occupied by goats vary among surveys, we recommend using sightability correction methods to decrease bias in population estimates from aerial surveys of mountain goats.     

Spatial and temporal factors influencing sightability of elk

Few tracking studies consider seasonal changes in ability to re-sight wildlife, despite potential for biases in sightability to mislead our interpretation of models of movement and abundance. We developed seasonal sightability models based on visual observations of radio-collared elk (Cervus elaphus) in Manitoba, Canada, through 6 seasons. We located 377 elk 8,862 times using aerial telemetry from 2002 to 2009. We tested the hypothesis that sites where we were able to visually observe radio-collared elk during aerial telemetry differed from sites where collared elk were known to be present but could not be sighted. Relationships varied with season and elk sightability was influenced by forest type, habitat openness, distance to edge, and time of day. Our results confirm that observers have the highest probability of detecting elk in early and late winter. However, factors such as day length, which increases by 64% during this period, suggest that fewer impediments to detection exist in late winter. Our findings reinforce the need to account for seasonal as well as spatial changes in habitat-specific sightability models. © 2011 The Wildlife Society.     

Spending degrees of freedom in a poor economy: A case study of building a sightability model for moose in northeastern Minnesota

Sightability models are binary logistic-regression models used to estimate and adjust for visibility bias in wildlife-population surveys. Like many models in wildlife and ecology, sightability models are typically developed from small observational datasets with many candidate predictors. Aggressive model-selection methods are often employed to choose a best model for prediction and effect estimation, despite evidence that such methods can lead to overfitting (i.e., selected models may describe random error or noise rather than true predictor–response curves) and poor predictive ability. We used moose (Alces alces) sightability data from northeastern Minnesota (2005–2007) as a case study to illustrate an alternative approach, which we refer to as degrees-of-freedom (df) spending: sample-size guidelines are used to determine an acceptable level of model complexity and then a pre-specified model is fit to the data and used for inference. For comparison, we also constructed sightability models using Akaike's Information Criterion (AIC) step-down procedures and model averaging (based on a small set of models developed using df-spending guidelines). We used bootstrap procedures to mimic the process of model fitting and prediction, and to compute an index of overfitting, expected predictive accuracy, and model-selection uncertainty. The index of overfitting increased 13% when the number of candidate predictors was increased from three to eight and a best model was selected using step-down procedures. Likewise, model-selection uncertainty increased when the number of candidate predictors increased. Model averaging (based on R = 30 models with 1–3 predictors) effectively shrunk regression coefficients toward zero and produced similar estimates of precision to our 3-df pre-specified model. As such, model averaging may help to guard against overfitting when too many predictors are considered (relative to available sample size). The set of candidate models will influence the extent to which coefficients are shrunk toward zero, which has implications for how one might apply model averaging to problems traditionally approached using variable-selection methods. We often recommend the df-spending approach in our consulting work because it is easy to implement and it naturally forces investigators to think carefully about their models and predictors. Nonetheless, similar concepts should apply whether one is fitting 1 model or using multi-model inference. For example, model-building decisions should consider the effective sample size, and potential predictors should be screened (without looking at their relationship to the response) for missing data, narrow distributions, collinearity, potentially overly influential observations, and measurement errors (e.g., via logical error checks). © 2011 The Wildlife Society.     

Estimation and Correction of Visibility Bias in Aerial Surveys of Wintering Ducks

Abstract: Incomplete detection of all individuals leading to negative bias in abundance estimates is a pervasive source of error in aerial surveys of wildlife, and correcting that bias is a critical step in improving surveys. We conducted experiments using duck decoys as surrogates for live ducks to estimate bias associated with surveys of wintering ducks in Mississippi, USA. We found detection of decoy groups was related to wetland cover type (open vs. forested), group size (1–100 decoys), and interaction of these variables. Observers who detected decoy groups reported counts that averaged 78% of the decoys actually present, and this counting bias was not influenced by either covariate cited above. We integrated this sightability model into estimation procedures for our sample surveys with weight adjustments derived from probabilities of group detection (estimated by logistic regression) and count bias. To estimate variances of abundance estimates, we used bootstrap resampling of transects included in aerial surveys and data from the bias-correction experiment. When we implemented bias correction procedures on data from a field survey conducted in January 2004, we found bias-corrected estimates of abundance increased 36–42%, and associated standard errors increased 38–55%, depending on species or group estimated. We deemed our method successful for integrating correction of visibility bias in an existing sample survey design for wintering ducks in Mississippi, and we believe this procedure could be implemented in a variety of sampling problems for other locations and species. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):808–813; 2008)     

A sightability model for correcting visibility and availability biases in standardized surveys of breeding burrowing owls in southwest agroecosystem environments

Unbiased estimates of burrowing owl populations (Athene cunicularia) are essential to achieving diverse management and conservation objectives. We conducted visibility trials and developed logistic regression models to identify and correct for visibility bias associated with single, vehicle-based, visual survey occasions of breeding male owls during daylight hours in an agricultural landscape in California between 30 April and 2 May 2007. Visibility was predicted best by a second-degree polynomial function of time of day and 7 categorical perch types. Probability of being visible was highest in the afternoon, and individuals that flushed, flew, or perched on hay bales were highly visible (>0.85). Visibility was lowest in agricultural fields (<0.46) and nonagricultural vegetation (<0.77). We used the results from this model to compute unbiased maximum likelihood estimates of visibility bias, and combined these with estimated probabilities of availability bias to validate our model by correcting for visibility and availability biases in 4 independent datasets collected during morning hours. Correcting for both biases produced reliable estimates of abundance in all 4 independent validation datasets. We recommend that estimates of burrowing owl abundance from surveys in the southwest United States correct for both visibility and availability biases. © 2011 The Wildlife Society.     

Variance of Stratified Survey Estimators With Probability of Detection Adjustments

Abstract: Estimates of wildlife population sizes are frequently constructed by combining counts of observed animals from a stratified survey of aerial sampling units with an estimated probability of detecting animals. Unlike traditional stratified survey designs, stratum-specific estimates of population size will be correlated if a common detection model is used to adjust counts for undetected animals in all strata. We illustrate this concept in the context of aerial surveys, considering 2 cases: 1) a single-detection parameter is estimated under the assumption of constant detection probabilities, and 2) a logistic-regression model is used to estimate heterogeneous detection probabilities. Naïve estimates of variance formed by summing stratum-specific estimates of variance may result in significant bias, particularly if there are a large number of strata, if detection probabilities are small, or if estimates of detection probabilities are imprecise. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):837–844; 2008)