Animal Density and Track Counts: Understanding the Nature of Observations Based on Animal Movements

Counting animals to estimate their population sizes is often essential for their management and conservation. Since practitioners frequently rely on indirect observations of animals, it is important to better understand the relationship between such indirect indices and animal abundance. The Formozov-Malyshev-Pereleshin (FMP) formula provides a theoretical foundation for understanding the relationship between animal track counts and the true density of species. Although this analytical method potentially has universal applicability wherever animals are readily detectable by their tracks, it has long been unique to Russia and remains widely underappreciated. In this paper, we provide a test of the FMP formula by isolating the influence of animal travel path tortuosity (i.e., convolutedness) on track counts. We employed simulations using virtual and empirical data, in addition to a field test comparing FMP estimates with independent estimates from line transect distance sampling. We verify that track counts (total intersections between animals and transects) are determined entirely by density and daily movement distances. Hence, the FMP estimator is theoretically robust against potential biases from specific shapes or patterns of animal movement paths if transects are randomly situated with respect to those movements (i.e., the transects do not influence animals’ movements). However, detectability (the detection probability of individual animals) is not determined simply by daily travel distance but also by tortuosity, so ensuring that all intersections with transects are counted regardless of the number of individual animals that made them becomes critical for an accurate density estimate. Additionally, although tortuosity has no bearing on mean track encounter rates, it does affect encounter rate variance and therefore estimate precision. We discuss how these fundamental principles made explicit by the FMP formula have widespread implications for methods of assessing animal abundance that rely on indirect observations.     

The mechanistic basis for higher‐order interactions and non‐additivity in competitive communities

Motivated by both analytical tractability and empirical practicality, community ecologists have long treated the species pair as the fundamental unit of study. This notwithstanding, the challenge of understanding more complex systems has repeatedly generated interest in the role of so‐called higher‐order interactions (HOIs) imposed by species beyond the focal pair. Here we argue that HOIs – defined as non‐additive effects of density on per capita growth – are best interpreted as emergent properties of phenomenological models (e.g. Lotka–Volterra competition) rather than as distinct ‘ecological processes’ in their own right. Using simulations of consumer‐resource models, we explore the mechanisms and system properties that give rise to HOIs in observational data. We demonstrate that HOIs emerge under all but the most restrictive of assumptions, and that incorporating non‐additivity into phenomenological models improves the quantitative and qualitative accuracy of model predictions. Notably, we also observe that HOIs derive primarily from mechanisms and system properties that apply equally to single‐species or pairwise systems as they do to more diverse communities. Consequently, there exists a strong mandate for further recognition of non‐additive effects in both theoretical and empirical research.     

Density‐dependent space use affects interpretation of camera trap detection rates

Camera traps (CTs) are an increasingly popular tool for wildlife survey and monitoring. Estimating relative abundance in unmarked species is often done using detection rate as an index of relative abundance, which assumes that detection rate has a positive linear relationship with true abundance. This assumption may be violated if movement behavior varies with density, but the degree to which movement behavior is density‐dependent across taxa is unclear. The potential confounding of population‐level relative abundance indices by movement would depend on how regularly, and by what magnitude, movement rate and home‐range size vary with density. We conducted a systematic review and meta‐analysis to quantify relationships between movement rate, home‐range size, and density, across terrestrial mammalian taxa. We then simulated animal movements and CT sampling to test the effect of contrasting movement scenarios on CT detection rate indices. Overall, movement rate and home‐range size were negatively correlated with density and positively correlated with one another. The strength of the relationships varied significantly between taxa and populations. In simulations, detection rates were related to true abundance but underestimated change, particularly for slower moving species with small home ranges. In situations where animal space use changes markedly with density, we estimate that up to thirty percent of a true change in relative abundance may be missed due to the confounding effect of movement, making trend estimation more difficult. The common assumption that movement remains constant across densities is therefore violated across a wide range of mammal species. When studying unmarked species using CT detection rates, researchers and managers should explicitly consider that such indices of relative abundance reflect both density and movement. Practitioners interpreting changes in camera detection rates should be aware that observed differences may be biased low relative to true changes in abundance. Further information on animal movement, or methods that do not depend on assumptions of density‐independent movement, may be required to make robust inferences on population trends.     

Co‐declining mammal–dung beetle faunas throughout the Atlantic Forest biome of South America

The millennial–scale evolutionary relationships between mammals and dung beetles have been eroded due to several drivers of contemporary biodiversity loss. Although some evidence of co‐decline has been shown for mammals and dung beetles at some Neotropical sites, a biome‐scale analysis for the entire Atlantic Forest of South America would strengthen our understanding of how relictual sets of mammal species can affect dung beetle co‐occurrences and co‐declines. We therefore collated hundreds of assemblages of both dung beetles and medium‐ to large‐bodied mammals throughout the world's longest tropical forest latitudinal gradient to examine to what extent mammal assemblages may exert a positive influence on dung beetle species composition and functional assembly, and whether this relationship is scale dependent. We also collated several climatic and other environmental variables to examine the degree to which they shape mammal–dung beetle relationships. The relationships between local mammal and dung beetle faunas were examined using regression models, variation partitioning, dissimilarity indices and ecological networks. We found a clear positive relationship between mammal and dung beetle species richness across this forest biome, indicating an ongoing process of mammal–dung beetle niche‐mediated co‐decline. We found a strong relationship between the species composition of both taxa, in which dung beetle species dissimilarity apparently track changes in mammalian dissimilarity, typically in 80% of all cases. Co‐variables such as phytomass and climatic variables also influenced mammal–dung beetle patterns of co‐decline along the Atlantic Forest. We conclude that dung beetle diversity and community assembly are shaped by the remaining co‐occurring mammal assemblages and their functional traits, and both groups were governed by environmental features. We emphasize that ecosystem‐wide effects of mammal population declines remain poorly understood both quantitatively and qualitatively, and curbing large vertebrate defaunation will ensure the persistence of co‐dependent species.     

The distribution,density and biomass density of lizards in a semi–arid environment of northern Kenya*

An attempt was made to determine the species composition, density and biomass density of lizards in some of the principal land units in South Turkana. Thirteen species were recorded. Density estimates were determined from quadrat sampling in representative habitats. Because the number of lizards active was found to vary with time of day and temperature, minimum density estimates were obtained by confining counts to peak activity periods. Biomass density was calculated from the product of species density and the mean population weight measured from shot specimens. The biomass density estimate of the lizard fauna was found to be about 4–5% of the large mammal fauna, and appreciably more in the more arid land units. The species composition of South Turkana lizards show affinities to the Somali fauna, though not to the extent of the East Rudolf fauna, suggesting a more recent penetration of arid–adapted species.     

A spatial theory for emergent multiple predator–prey interactions in food webs

Predator–prey interaction is inherently spatial because animals move through landscapes to search for and consume food resources and to avoid being consumed by other species. The spatial nature of species interactions necessitates integrating spatial processes into food web theory and evaluating how predators combine to impact their prey. Here, we present a spatial modeling approach that examines emergent multiple predator effects on prey within landscapes. The modeling is inspired by the habitat domain concept derived from empirical synthesis of spatial movement and interactions studies. Because these principles are motivated by synthesis of short‐term experiments, it remains uncertain whether spatial contingency principles hold in dynamical systems. We address this uncertainty by formulating dynamical systems models, guided by core habitat domain principles, to examine long‐term multiple predator–prey spatial dynamics. To describe habitat domains, we use classical niche concepts describing resource utilization distributions, and assume species interactions emerge from the degree of overlap between species. The analytical results generally align with those from empirical synthesis and present a theoretical framework capable of demonstrating multiple predator effects that does not depend on the small spatial or temporal scales typical of mesocosm experiments, and help bridge between empirical experiments and long‐term dynamics in natural systems.     

Context‐dependency and anthropogenic effects on individual plant–frugivore networks

Anthropogenic activities, such as grazing by domestic animals, are considered drivers of environmental changes that may influence the structure of interaction networks. The study of individual‐based networks allows testing how species‐level interaction patterns emerge from the pooled interaction modes of individuals within populations. Exponential random graph models (ERGMs) examine the global structure of networks by allowing the inclusion of specific node (i.e. interacting partners) properties as explanatory covariates. Here we assessed the structure of individual plant–frugivore interaction networks and the ecological variables that influence the mode of interactions under different land‐use (grazed versus ungrazed protected areas). We quantified the number of visits, the number of fruits removed per visit and the interaction strength of mammal frugivore species at each individual tree. Additionally we quantified ecological variables at the individual, microhabitat, neighborhood and habitat scales that generated interaction network structure under the different land uses. Individual plant–frugivore networks were significantly modular in both land uses but the number of modules was higher in the grazed areas. We found interaction networks for grazed and ungrazed lands were structured by phenotypic traits of individual trees, by the microhabitat beneath the tree canopy and were affected by habitat modifications of anthropogenic origin. The neighborhood surrounding each individual plant influenced plant–frugivore interactions only at the grazed‐land trees. We conclude that anthropogenic land uses influence the topological patterns of plant–frugivore networks and the frugivore visitation to trees through modification of both habitat complexity and the ecological traits underlying interactions between individual plants and frugivore species.     

Community‐based wildlife management area supports similar mammal species richness and densities compared to a national park

Community‐based conservation models have been widely implemented across Africa to improve wildlife conservation and livelihoods of rural communities. In Tanzania, communities can set aside land and formally register it as Wildlife Management Area (WMA), which allows them to generate revenue via consumptive or nonconsumptive utilization of wildlife. The key, yet often untested, assumption of this model is that economic benefits accrued from wildlife motivate sustainable management of wildlife. To test the ecological effectiveness (here defined as persistence of wildlife populations) of Burunge Wildlife Management Area (BWMA), we employed a participatory monitoring approach involving WMA personnel. At intermittent intervals between 2011 and 2018, we estimated mammal species richness and population densities of ten mammal species (African elephant, giraffe, buffalo, zebra, wildebeest, waterbuck, warthog, impala, Kirk's dik‐dik, and vervet monkey) along line transects. We compared mammal species accumulation curves and density estimates with those of time‐matched road transect surveys conducted in adjacent Tarangire National Park (TNP). Mammal species richness estimates were similar in both areas, yet observed species richness per transect was greater in TNP compared to BWMA. Species‐specific density estimates of time‐matched surveys were mostly not significantly different between BWMA and TNP, but elephants occasionally reached greater densities in TNP compared to BWMA. In BWMA, elephant, wildebeest, and impala populations showed significant increases from 2011 to 2018. These results suggest that community‐based conservation models can support mammal communities and densities that are similar to national park baselines. In light of the ecological success of this case study, we emphasize the need for continued efforts to ensure that the BWMA is effective. This will require adaptive management to counteract potential negative repercussions of wildlife populations on peoples' livelihoods. This study can be used as a model to evaluate the effectiveness of wildlife management areas across Tanzania.     

Mammal population densities at a global scale are higher in human‐modified areas

Global landscapes are changing due to human activities with consequences for both biodiversity and ecosystems. For single species, terrestrial mammal population densities have shown mixed responses to human pressure, with both increasing and decreasing densities reported in the literature. How the impacts of human activities on mammal populations translates into altered global density patterns remains unclear. Here we aim to disentangle the effect of human impacts on large‐scale patterns of mammal population densities using a global dataset of 6729 population density estimates for 468 mammal species (representing 59% and 44% of mammalian orders and families). We fitted a mixed effect model to explain the variation in density based on a 1‐degree resolution as a function of the human footprint index (HFI), a global proxy of direct and indirect human disturbances, while accounting for body mass, trophic level and primary productivity (normalized vegetation index; NDVI). We found a significant positive relationship between population density and HFI, where population densities were higher in areas with a higher HFI (e.g. agricultural or suburban areas – no populations were located in very high HFI urban areas) compared to areas with a low HFI (e.g. wilderness areas). We also tested the effect of the individual components of the HFI and still found a consistent positive effect. The relationships remained positive even across populations of the same species, although variability among species was high. Our results indicate shifts in mammal population densities in human modified landscapes, which is due to the combined effect of species filtering, increased resources and a possible reduction in competition and predation. Our study provides further evidence that macroecological patterns are being altered by human activities, where some species will benefit from these activities, while others will be negatively impacted or even extirpated.     

Density influences accuracy of model‐based estimates for a forest songbird

Golden‐cheeked Warblers (Setophaga chrysoparia) are endangered songbirds that breed exclusively in the Ashe juniper (Juniperus ashei) and oak (Quercus spp.) woodlands of central Texas. Despite being the focus of numerous studies, we still know little about the size of the range‐wide breeding population and how density varies across the spectrum of juniper co‐dominated woodlands. Models that have been tested and shown to be accurate are needed to help develop management and conservation guidelines. We evaluated the accuracy and bias of density estimates from binomial mixture models, the dependent double‐observer method, and distance sampling by comparing them to actual densities determined by intensive territory monitoring on plots in the Balcones Canyonlands Preserve, Austin, Texas. We found that the binomial mixture models consistently overestimated density by 1.1–3.2 times (actual density = 0.07–0.46 males/ha), and the other two models overestimated by 1.1–29.8 times at low density and underestimated by 0.5–0.9 times at high density plots (actual density = 0.01–0.46 males/ha). The magnitude of error for all models was greatest at sites with few or no birds (<0.15 males/ha), with model performance improving as actual density increased. These non‐linear relationships indicate a lack of sensitivity with respect to true changes in density. Until systematic evaluation demonstrates that models such as those we tested provide accurate and unbiased density estimates for a given species over space and time, we recommend additional field tests to validate model‐based estimates. Continued model validation and refinement of point‐count methods are needed until accurate estimates are obtained across the density spectrum for Golden‐cheeked Warblers and other songbird species.     

Characterizing abundance–occupancy relationships: there is no artefact

Positive abundance–occupancy relationships (AORs) are among the most general macroecological patterns: locally common species are regionally widespread, locally rare species are regionally restricted. In a recent contribution, Wilson (Global Ecology and Biogeography, 2011, 20 , 193–202) made three claims: (1) that AORs are critically dependent on the method used to calculate average abundance; (2) averaging abundance over occupied sites tends to lead to a very high incidence of negative relationships; (3) this represents a statistical artefact that should be considered in studies of AORs. Here we show that this outcome arises in Wilson's simulations purely due to an arbitrary choice of occupancy models and parameter ranges. The resulting negative relationships are not statistical artefacts, but are easily interpreted in terms of spatial aggregation in abundant species. The fact that empirical evidence fails to support a high prevalence of negative AORs suggests, however, that such parameter combinations arise only rarely in nature. We conclude that simulations that are based on untested assumptions, and that produce patterns unsupported by empirical evidence, have limited use in characterizing AORs, and add little to understanding of the processes driving important relationships between local population size and regional occupancy.     

Random Encounter and Staying Time Model Testing with Human Volunteers

Ecology and management programs designed to track population trends over time increasingly are using passive monitoring methods to estimate terrestrial mammal densities. Researchers use motion-sensing cameras in mammal studies because they are cost-effective and advances in statistical methods incorporate motion-sensing camera data to estimate mammal densities. Density estimation involving unmarked individuals, however, remains challenging and empirical tests of statistical models are relatively rare. We tested the random encounter and staying time model (REST), a new means of estimating the density of an unmarked population, using human volunteers and simulated camera surveys. The REST method produced unbiased estimates of density, regardless of changes in human abundance, movement rates, home range sizes, or simulated camera effort. These advances in statistical methods when applied to motion-sensing camera data provide innovative avenues of large-mammal monitoring that have the potential to be applied to a broad spectrum of conservation and management studies, provided assumptions for the REST method are rigorously tested and met. © 2020 The Wildlife Society.     

Corrigendum to Streicker et al. (2013) Differential sources of host species heterogeneity influence the transmission and control of multi‐host parasites

In a recent article, we described a conceptual and analytical model to identify the key host species for parasite transmission in multi‐host communities and used data from 11 gastro‐intestinal parasites infecting up to five small mammal host species as an illustrative example of how the framework could be applied. A limitation of these empirical data was uncertainty in the identification of parasite species using egg/oocyst morphology, which could overestimate parasite sharing between host species. Here, we show that the key results of the original analysis, namely that (1) parasites naturally infect multiple host species, but typically rely on a small subset of infected host species for long‐term maintenance, (2) that different mechanisms underlie how particular host species dominate transmission and (3) that these different mechanisms influence the predicted efficiency of disease control measures, are robust to analysis of a smaller subset of host–parasite combinations that we have greatest confidence in identifying. We further comment briefly on the need for accurate parasite identification, ideally using molecular techniques to quantify cross‐species transmission and differentiate covert host specificity from true host generalism.     

Species traits and interaction rules shape a species‐rich seed‐dispersal interaction network

Species phenotypic traits affect the interaction patterns and the organization of seed‐dispersal interaction networks. Understanding the relationship between species characteristics and network structure help us understand the assembly of natural communities and how communities function. Here, we examine how species traits may affect the rules leading to patterns of interaction among plants and fruit‐eating vertebrates. We study a species‐rich seed‐dispersal system using a model selection approach to examine whether the rules underlying network structure are driven by constraints in fruit resource exploitation, by preferential consumption of fruits by the frugivores, or by a combination of both. We performed analyses for the whole system and for bird and mammal assemblages separately, and identified the animal and plant characteristics shaping interaction rules. The structure of the analyzed interaction network was better explained by constraints in resource exploitation in the case of birds and by preferential consumption of fruits with specific traits for mammals. These contrasting results when looking at bird–plant and mammal–plant interactions suggest that the same type of interaction is organized by different processes depending on the assemblage we focus on. Size‐related restrictions of the interacting species (both for mammals and birds) were the most important factors driving the interaction rules. Our results suggest that the structure of seed‐dispersal interaction networks can be explained using species traits and interaction rules related to simple ecological mechanisms.     

Variability in 20th century climate change reconstructions and its consequences for predicting geographic responses of California mammals

Empirical species distribution models are widely used to predict the effects of climate change on biodiversity distribution but rely on multiple assumptions about the certainty of the locality and climate data. Here, we assess the effect of historical climate data variability when forecasting geographic responses of California mammals to 20th century climate change. We first used two methods to derive gridded climate surfaces from weather station data (ANUSPLIN and PRISM) representing two sampling eras: historic (1900–1940) and current (1980–2005). We then used the two sources of climate data in conjunction with a maximum entropy algorithm (MAXENT) to predict both the historic and current distributions of all major mammal species vouchered historically in California. Results indicate that levels of disagreement between the two climate datasets are considerably greater in the historical era than in the current era. For the bioclimatic variables used in modeling historical mammal distributions, precipitation variables were less concordant than temperature variables. These discrepancies are reflected in the low agreement between historic mammal range predictions and further propagated when the historic models are projected to present day. Nonetheless, some common patterns exist across mammal species and climate estimates. Range stability is the most common prediction between the two eras, followed by expansion and contraction. Jepson ecoregions with relatively high levels of range stability include parts of the Great Central Valley and Sierra Nevada, while other parts of the Central Valley, the Sonoran desert, and Central- and Southwestern California yield predictions of range shifts. Historical species distribution modeling can greatly inform studies attempting to describe how species will continue to move geographically in response to future changes in climate. We suggest that alternative estimates of historical climate and their uncertainties are ultimately required in order to provide a quantitative measure of the confidence in predicted changes in distribution.     

The impact of the megafauna extinctions on savanna woody cover in South America

Has land surface cover in South America been impacted by the loss of most large herbivores following the severe Pleistocene and Early Holocene megafauna extinctions on this continent? Here, we estimate how mean savanna woody biomass may have changed in the Americas following these extinctions by creating an empirical model to understand how large herbivores impact savanna woody biomass. To create this empirical model, we combine a large recently published dataset of savanna woody cover from Lehmann et al. (2014) (n = 2154 plots) with estimates of mammals ranges and weights from the IUCN database. We evaluate how variables such as number of megaherbivores (mammal species ≥ 1000 kg), log10 sum species weights, and total number of mammal species predict changes to woody cover by using both ordinary least squares regression analysis (OLS) and simultaneous auto‐regressive (SAR) analysis to control for spatial autocorrelation. Both number of megaherbivores and log10 sum species weights, which both disproportionately weight for megaherbivores, significantly explained much (～ 5–13%) variance in woody cover, but the third variable weighting all animals equally, did not. We then combined these biotic variables with abiotic variables such as temperature, precipitation, and fire frequency to create a model predicting 36% of the variance of savanna woody cover. We used this model combined with estimated range maps of extinct South American megafauna to estimate that had those South American megafauna not gone extinct, total savanna woody cover in South America could possibly have decreased by ～ 29% and that savannas would likely have been more open like current African savannas.     

Mammals of Borneo – small size on a large island

Aim Island mammals have featured prominently in models of the evolution of body size. Most of these models examine size evolution across a wide range of islands in order to test which island characteristics influence evolutionary pathways. Here, we examine the mammalian fauna of a single island, Borneo, where previous work has detected that some mammal species have evolved a relatively small size. We test whether Borneo is characterized by smaller mammals than adjacent areas, and examine possible causes for the different trajectories of size evolution between different Bornean species. Location Sundaland: Borneo, Sumatra, Java and the Malay/Thai Peninsula. Methods We compared the mammalian body size frequency distributions in the four areas to examine whether the large mammal fauna of Borneo is more depauperate than elsewhere. We measured specimens belonging to 54 mammal species that are shared between Borneo and any of the other areas in order to determine whether there is an intraspecific tendency for Bornean mammals to evolve small body size. Using data on diet, body size and geographical ranges we examine factors that are thought to influence body size. Results Borneo has fewer large mammals than the other areas, but this is not statistically significant. Large Bornean mammals are significantly smaller than their conspecifics in the other regions, while there are no differences between the body sizes of mammals on Sumatra, Java and the Malay/Thai Peninsula. The finding that large mammals show the greatest size difference between Borneo and elsewhere contrasts with some models of size evolution on islands of different areas. Diet does not correlate with the degree of size reduction. Sunda region endemics show a weaker tendency to be small on Borneo than do widespread species. Main conclusions We suggest that soil quality may drive size evolution by affecting primary productivity. On Borneo, where soils are generally poor in nutrients, this may both limit biomass and cause mammals to be reduced in body size. We hypothesize that widespread species respond to low resource abundance by reducing in size, while endemic elements of the fauna have had longer to adjust to local conditions by altering their behaviour, physiology and/or ecology, and are thus similar in size across the region.     

Species co‐occurrence analysis: pairwise versus matrix‐level approaches

Veech (2013, Global Ecology and Biogeography, 22 , 252–260) introduced a formula to calculate the probability of two species co‐occurring in various sites under the assumption of statistical independence between the two distributional patterns. He presented his model as a new procedure, a ‘pairwise approach’, different from analyses of whole presence–absence matrices to examine patterns of co‐occurrence. Here I show that: (1) Veech's method is identical to Fisher's exact test, a standard procedure for measuring the statistical association between two discrete variables; (2) in a broad sense, the pairwise approach is very similar to early analyses of spatial association, such as the one advanced by Forbes in 1907; (3) implicit in Veech's formula is a sampling scheme that is indistinguishable from well‐known matrix‐level null models that randomize the distribution of species among equiprobable sites; (4) pairwise co‐occurrence patterns can be analysed using any matrix‐level null model, so pairwise comparisons are not limited to using Veech's formula. The methodological distinction that Veech proposed between pairwise and matrix‐level approaches does not in fact exist, although the conceptual distinction between the two approaches is still a debated topic.     

A local evaluation of the individual state‐space to scale up Bayesian spatial capture–recapture

Spatial capture–recapture models (SCR) are used to estimate animal density and to investigate a range of problems in spatial ecology that cannot be addressed with traditional nonspatial methods. Bayesian approaches in particular offer tremendous flexibility for SCR modeling. Increasingly, SCR data are being collected over very large spatial extents making analysis computational intensive, sometimes prohibitively so. To mitigate the computational burden of large‐scale SCR models, we developed an improved formulation of the Bayesian SCR model that uses local evaluation of the individual state‐space (LESS). Based on prior knowledge about a species’ home range size, we created square evaluation windows that restrict the spatial domain in which an individual's detection probability (detector window) and activity center location (AC window) are estimated. We used simulations and empirical data analyses to assess the performance and bias of SCR with LESS. LESS produced unbiased estimates of SCR parameters when the AC window width was ≥5σ (σ: the scale parameter of the half‐normal detection function), and when the detector window extended beyond the edge of the AC window by 2σ. Importantly, LESS considerably decreased the computation time needed for fitting SCR models. In our simulations, LESS increased the computation speed of SCR models up to 57‐fold. We demonstrate the power of this new approach by mapping the density of an elusive large carnivore—the wolverine (Gulo gulo)—with an unprecedented resolution and across the species’ entire range in Norway (> 200,000 km²). Our approach helps overcome a major computational obstacle to population and landscape‐level SCR analyses. The LESS implementation in a Bayesian framework makes the customization and fitting of SCR accessible for practitioners working at scales that are relevant for conservation and management.