No abstract

Data are presented on the demography and reproductive success of cheetahs (Acinonyx jubatus) living on the Serengeti Plains, Tanzania over a 25-year period. Average age at independence was 17.1 months, females gave birth to their ®rst litter at approximately 2.4 years old, interbirth interval was 20.1 months, and average litter size at independence was 2.1 cubs. Females who survived to independence lived on average 6.2 years while minimum male average longevity was 2.8 years for those born in the study area and 5.3 years for immigrants, with a large proportion of males dispersing out of the Plains population. Females produced on average only 1.7 cubs to independence in their entire lifetime and their average reproductive rates were 0.36 cubs per year or 0.17 litters per year to independence. Variance in lifetime reproductive success in the cheetah is similar to that of other mammals.No signi®cant negative correlations were found between adult cheetah population size and numbers of cubs reaching independence, implying that the Plains population had not reached carrying capacity. Annual numbers of adult female cheetahs only were correlated with rainfall. Adult female cheetah numbers were not correlated with adult female lion numbers on the Plains, however, reproductive rates of cheetahs were negatively correlated with the presence of lions while cheetahs had cubs. Moreover, cheetah reproductive success was lower during the period of high lion abundance (1980±1994) than during the earlier period of relatively few lions (1969±1979). Litter size at independence dropped from 2.5 to 2.0, lifetime reproductive success declined from 2.1 to 1.6 cubs reared to independence, and the reproductive rate (cubs/year) decreased from 0.42 to 0.36 from the earlier to the later period.Cheetah reproductive success showed little association with the presence of Thomson's gazelle at sightings except for a negative correlation between large numbers of gazelle (200±500) and reproductive success possibly because hunting success decreases with increasing prey herd size, or because cheetahs always lose in direct competition with other predators which are attracted to large congregations of prey. In addition, cheetah reproductive success was negatively correlated with the presence of Grant's gazelles (11 or more) perhaps because Grant's gazelles were more likely to occur consistently in dry areas.

Recently introduced unmarked spatial capture-recapture (SCR), spatial mark-resight (SMR), and 2-flank spatial partial identity models (SPIM) extend the domain of SCR to populations or observation systems that do not always allow for individual identity to be determined with certainty. For example, some species do not have natural marks that can reliably produce individual identities from photographs, and some methods of observation produce partial identity samples as is the case with remote cameras that sometimes produce single flank photographs. These models share the feature that they probabilistically resolve the uncertainty in individual identity using the spatial location where samples were collected. Spatial location is informative of individual identity in spatially structured populations with home range sizes smaller than the extent of the trapping array because a latent identity sample is more likely to have been produced by an individual living near the trap where it was recorded than an individual living further away from the trap. Further, the level of information about individual identity that a spatial location contains is determined by two key ecological concepts, population density and home range size. The number of individuals that could have produced a latent or partial identity sample increases as density and home range size increase because more individual home ranges will overlap any given trap. We show this uncertainty can be quantified using a metric describing the expected magnitude of uncertainty in individual identity for any given population density and home range size, the Identity Diversity Index (IDI). We then show that the performance of latent and partial identity SCR models varies as a function of this index and produces imprecise and biased estimates in many high IDI scenarios when data are sparse. We then extend the unmarked SCR model to incorporate partially identifying covariates which reduce the level of uncertainty in individual identity, increasing the reliability and precision of density estimates, and allowing reliable density estimation in scenarios with higher IDI values and with more sparse data. We illustrate the performance of this "categorical SPIM" via simulations and by applying it to a black bear data set using microsatellite loci as categorical covariates, 2 . CC-BY-NC-ND4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/265678 doi: bioRxiv preprint first posted online Feb. 15, 2018;where we reproduce the full data set estimates with only slightly less precision using fewer loci than necessary for confident individual identification. The categorical SPIM offers an alternative to using probability of identity criteria for classifying genotypes as unique, shifting the "shadow effect", where more than one individual in the population has the same genotype, from a source of bias to a source of uncertainty. We discuss the difficulties th...

We estimated leopard (Panthera pardus fusca) abundance and density in the Bhabhar physiographic region in Parsa Wildlife Reserve, Nepal. The camera trap grid, covering sampling area of 289 km2 with 88 locations, accumulated 1,342 trap nights in 64 days in the winter season of 2008-2009 and photographed 19 individual leopards. Using models incorporating heterogeneity, we estimated 28 (±SE 6.07) and 29.58 (±SE 10.44) leopards in Programs CAPTURE and MARK. Density estimates via 1/2 MMDM methods were 5.61 (±SE 1.30) and 5.93 (±SE 2.15) leopards per 100 km2 using abundance estimates from CAPTURE and MARK, respectively. Spatially explicit capture recapture (SECR) models resulted in lower density estimates, 3.78 (±SE 0.85) and 3.48 (±SE 0.83) leopards per 100 km2, in likelihood based program DENSITY and Bayesian based program SPACECAP, respectively. The 1/2 MMDM methods have been known to provide much higher density estimates than SECR modelling techniques. However, our SECR models resulted in high leopard density comparable to areas considered better habitat in Nepal indicating a potentially dense population compared to other sites. We provide the first density estimates for leopards in the Bhabhar and a baseline for long term population monitoring of leopards in Parsa Wildlife Reserve and across the Terai Arc.

Camera trapping surveys frequently capture individuals whose identity is only known from a single flank. The most widely used methods for incorporating these partial identity individuals into density analyses discard some of the partial identity capture histories, reducing precision, and while not previously recognized, introducing bias. Here, we present the spatial partial identity model (SPIM), which uses the spatial location where partial identity samples are captured to probabilistically resolve their complete identities, allowing all partial identity samples to be used in the analysis. We show that the SPIM out-performs other analytical alternatives. We then apply the SPIM to an ocelot data set collected on a trapping array with double-camera stations and a bobcat data set collected on a trapping array with single-camera stations. The SPIM improves inference in both cases and in the ocelot example, individual sex determined from photographs is used to further resolve partial identities, one of which is resolved to near certainty. The SPIM opens the door for the investigation of trapping designs that deviate from the standard 2 camera design, the combination of other data types between which identities cannot be deterministically linked, and can be extended to the problem of partial genotypes. * Corresponding Author 1 2 AUGUSTINE ET AL.1. Introduction. The inferential goal of capture-recapture studies is to estimate population density, D, or abundance, N , in the presence of imperfect detection. Individuals are either naturally or manually marked and subjected to repeated capture attempts in order to estimate their capture probability and thus D or N . Generally, capture-recapture models for wildlife species regard the individual identity of each capture event as known; however in practice, the identities of individuals for some capture events can be ambiguous or erroneous. In live-capture studies, tags can be lost. In camera trapping studies, researchers often obtain partial identity samples -left-only and right-only photographs that cannot be deterministically linked. In genetic capture-recapture studies, partial genotypes and allelic dropout can lead to partial identification or misidentification, respectively. Statistical models have been developed to address the problem of imperfect identification in live capture using double tagging (e.g. Wimmer et al., 2013) and in camera trap and genetic capture-recapture studies by regarding the complete identification of partial or potentially erroneous samples as latent and specifying models for both the capture-recapture process and the imperfect observation process conditional on the capture (e.g. McClintock et al., 2013;Bonner and Holmberg, 2013;Wright et al., 2009). However, relatively little attention has been paid to one of the most important determinants of sample identity-the spatial location where it was collected. The identity of ambiguous samples should more likely match the identity of other samples collected closer together in space than those collecte...