Identifying appropriate indicator species for the impact of deer on forest vegetation is crucial for forest management in deer habitats and is required to be sensitive to temporal and spatial variations in deer density. Dryopteris crassirhizoma was selected as a new indicator to evaluate the response to these variations. We examined the population-level characteristics, morphological characteristics at the individual level, and grazing intensity of D. crassirhizoma at temporally different deer density sites in Hokkaido, Japan. The response of D. crassirhizoma to spatial variation in deer density was also examined within and between two regions in Hokkaido, Japan. Although the population-level characteristics and morphological characteristics did not significantly respond to short-term decreases in deer density, grazing intensity significantly decreased with decreasing deer density. The grazing intensity was also positively related to the spatial variation of deer density within both regions, but the estimated coefficient of the grazing intensity differed between regions. We concluded that D. crassirhizoma can be a useful indicator species of the impact of deer on forest vegetation. The grazing intensity of the indicator species was sensitive to temporal and spatial variations in deer density within the region.

We anticipate that unmanned aerial vehicles will become popular wildlife survey platforms. Because detecting animals from the air is imperfect, we develop a mark-recapture line transect method using two digital cameras, possibly mounted on one aircraft, which cover the same area with a short time delay between them. Animal movement between the passage of the cameras introduces uncertainty in individual identity, so individual capture histories are unobservable and are treated as latent variables. We obtain the likelihood for mark-recapture line transects without capture histories by automatically enumerating all possibilities within segments of the transect that contain ambiguous identities, instead of attempting to decide identities in a prior step. We call this method \"Latent Capture-history Enumeration\" (LCE). We include an availability model for species that are periodically unavailable for detection, such as cetaceans that are undetectable while diving. External data are needed to estimate the availability cycle length, but not the mean availability rate, if the full availability model is employed. We compare the LCE method with the recently developed cluster capture-recapture method (CCR), which uses a Palm likelihood approximation, providing the first comparison of CCR with maximum likelihood. The LCE estimator has slightly lower variance, more so as sample size increases, and close to nominal coverage probabilities. Both methods are approximately unbiased. We illustrate with semisynthetic data from a harbor porpoise survey.

The line transect method is one of the main methods used to estimate primate densities. Several protocols have been proposed to analyze the data recorded under this method but none of them have been widely accepted since there is a considerable controversy about their respective accuracy. In this study, densities of the black colobus monkeyColobus satanas calculated using eight different protocols were compared with the actual density given by the home range method. Rates of polyspecific associations were also compared. The two most accurate estimates of group density (under- or overestimation <10%) were yielded by the protocol that used the maximum transect-to-animal distance and by that of using a fixed distance of 100 m. These protocols, however, underestimated individual density because counts performed from transects underestimated by 23% the average group size. The six other protocols overestimated group density by 20-195%. Factors that could explain these overestimation were discussed. Because histogram of sighting frequency showed several classes of distances with no records and because groups have been detected as far as 160 m, we suggested that the uneven topography of the study site increased the variability of the sighting distances. Combined with a relatively low number of sightings (n=23), this did not allow to identify a sharp detection distance. Rates of polyspecific association found with the two methods were similar. We recommend to investigate the influence of the topography for bias in density estimates when using the line transect method.

South America holds 30% of the world\'s avifauna, with the Atlantic Forest representing one of the richest regions of the Neotropics. Here we have compiled a data set on Brazilian Atlantic Forest bird occurrence (150,423) and abundance samples (N = 832 bird species; 33,119 bird individuals) using multiple methods, including qualitative surveys, mist nets, point counts, and line transects). We used four main sources of data: museum collections, on-line databases, literature sources, and unpublished reports. The data set comprises 4,122 localities and data from 1815 to 2017. Most studies were conducted in the Florestas de Interior (1,510 localities) and Serra do Mar (1,280 localities) biogeographic sub-regions. Considering the three main quantitative methods (mist net, point count, and line transect), we compiled abundance data for 745 species in 576 communities. In the data set, the most frequent species were Basileuterus culicivorus, Cyclaris gujanensis, and Conophaga lineata. There were 71 singletons, such as Lipaugus conditus and Calyptura cristata. We suggest that this small number of records reinforces the critical situation of these taxa in the Atlantic Forest. The information provided in this data set can be used for macroecological studies and to foster conservation strategies in this biodiversity hotspot. No copyright restrictions are associated with the data set. Please cite this Data Paper if data are used in publications and teaching events.

Line transect surveys are widely used in Neotropical rainforests to estimate the population abundance of medium- and large-sized vertebrates. The use of indices such as encounter rate has been criticized because the probability of animal detection may fluctuate due to the heterogeneity of environmental conditions among sites. In addition, the morphological and behavioral characteristics (biological traits) of species affect their detectability. In this study, we compared the extent to which environmental conditions and species\' biological traits bias abundance estimates in terra firme rainforests in French Guiana. The selected environmental conditions included both physical conditions and forest structure covariates, while the selected biological traits included the morphological and behavioral characteristics of species. We used the distance sampling method to model the detection probability as an explicit function of environmental conditions and biological traits and implemented a model selection process to determine the relative importance of each group of covariates. Biological traits contributed to the variability of animal detectability more than environmental conditions, which had only a marginal effect. Detectability was best for large animals with uniform or disruptive markings that live in groups in the canopy top. Detectability was worst for small, solitary, terrestrial animals with mottled markings. In the terra firme rainforests that represent ~80% of the Amazonia and Guianas regions, our findings support the use of relative indices such as the encounter rate to compare population abundance between sites in species-specific studies. Even though terra firme rainforests may appear similar between regions of Amazonia and the Guianas, comparability must be ensured, especially in forests disturbed by human activity. The detection probability can be used as an indicator of species\' vulnerability to hunting and, thus, to the risk of local extinction. Only a few biological trait covariates are required to correctly estimate the detectability of the majority of medium- and large-sized vertebrates. Thus, a biological trait model could be useful in predicting the detection probabilities of rare, uncommon, or localized species for which few data are available to fit the detection function.

Conventional distance sampling (CDS) methods assume that animals are uniformly distributed in the vicinity of lines or points. But when animals move in response to observers before detection, or when lines or points are not located randomly, this assumption may fail. By formulating distance sampling models as survival models, we show that using time to first detection in addition to perpendicular distance (line transect surveys) or radial distance (point transect surveys) allows estimation of detection probability, and hence density, when animal distribution in the vicinity of lines or points is not uniform and is unknown. We also show that times to detection can provide information about failure of the CDS assumption that detection probability is 1 at distance zero. We obtain a maximum likelihood estimator of line transect survey detection probability and effective strip half-width using times to detection, and we investigate its properties by simulation in situations where animals are nonuniformly distributed and their distribution is unknown. The estimator is found to perform well when detection probability at distance zero is 1. It allows unbiased estimates of density to be obtained in this case from surveys in which there has been responsive movement prior to animals coming within detectable range. When responsive movement continues within detectable range, estimates may be biased but are likely less biased than estimates from methods that assuming no responsive movement. We illustrate by estimating primate density from a line transect survey in which animals are known to avoid the transect line, and a shipboard survey of dolphins that are attracted to it.

We develop estimators for line transect surveys of animals that are stochastically unavailable for detection while within detection range. The detection process is formulated as a hidden Markov model with a binary state-dependent observation model that depends on both perpendicular and forward distances. This provides a parametric method of dealing with availability bias when estimates of availability process parameters are available even if series of availability events themselves are not. We apply the estimators to an aerial and a shipboard survey of whales, and investigate their properties by simulation. They are shown to be more general and more flexible than existing estimators based on parametric models of the availability process. We also find that methods using availability correction factors can be very biased when surveys are not close to being instantaneous, as can estimators that assume temporal independence in availability when there is temporal dependence.