Extrapolating population size from the occupancy-abundance relationship and the scaling pattern of occupancy

Habitat occupancy of sloth bear Melursus ursinus in Chitwan National Park, Nepal

Mammals have experienced a massive decline in their populations and geographic ranges worldwide. The sloth bear, Melursus ursinus (Shaw, 1791), is one of many species facing conservation threats. Despite being endangered in Nepal, decades of inattention to the situation have hindered their conservation and management. We assessed the distribution and patterns of habitat use by sloth bears in Chitwan National Park (CNP), Nepal. We conducted sign surveys from March to June, 2020, in 4 × 4 km grids (n = 45). We collected detection/non-detection data along a 4-km trail that was divided into 20 continuous segments of 200 m each. We obtained environmental, ecological, and anthropogenic covariates to understand determinants of sloth bear habitat occupancy. The data were analyzed using the single-species single-season occupancy method, with a spatially correlated detection. Using repeated observations, these models accounted for the imperfect detectability of the species to provide robust estimates of habitat occupancy. The model-averaged occupancy estimate for the sloth bear was 69% and the detection probability was 0.25. The probability of habitat occupancy by sloth bears increased with the presence of termites and fruits and in rugged, dry, open, undisturbed habitats. Our results indicate that the sloth bear is elusive, functionally unique, and widespread in CNP. Future conservation interventions and action plans aimed at sloth bear management must adequately consider their habitat requirements.

Indigenous-led camera-trap research on traditional territories informs conservation decisions for resource extraction

The resource extraction that powers global economies is often manifested in Indigenous Peoples’ territories. Indigenous Peoples living on the land are careful observers of resulting biodiversity changes, and Indigenous-led research can provide evidence to inform conservation decisions. In the Nearctic western boreal forest, landscape change from forest harvesting and petroleum extraction is intensive and extensive. A First Nations community in the Canadian oil sands co-created camera-trap research to explore observations of presumptive species declines, seeking to identify the relative contributions of different industrial sectors to changes in mammal distributions. Camera data were analyzed via generalized linear models in a model-selection approach. Multiple forestry and petroleum extraction features positively and negatively affected boreal mammal species. Pipelines had the greatest negative effect size (for wolves), whereas well sites had a large positive effect size for multiple species, suggesting the energy sector as a target for co-management. Co-created research reveals spatial relationships of disturbance, prey, and predators on Indigenous traditional territories. It provides hypotheses, tests, and interpretations unique to outside perspectives; Indigenous participation in conservation management of their territories scales up to benefit global biodiversity conservation.

Temporal changes in abundance-occupancy relationships over 40 years

Abundance-occupancy (A-O) relationships are widely documented for many organismal groups and regions, and have been used to gain an understanding of regional population and community trends. Monitoring changes in abundance and occupancy over time may be what is required to document changes in conservation status and needs for some species, communities, or areas.We hypothesize that if there is a higher proportion of declining species in one group of species compared with another (e.g., migratory species vs. permanent residents), then a consequence of that difference will be vastly different abundance-occupancy relationships. If this difference persists through time, then the resulting A-O relationships between the groups will continue to diverge.For neotropical migrants, short-distance migrants, and permanent resident birds of North America, we assess the numbers of declining species over 1969-2009. We further test for differences in the A-O relationship across these three groups, and in rates of change in abundance and occupancy separately.We find significant differences in numbers of declining species across the migratory groups, a significant decline in the A-O relationship for permanent residents, a significant increase for Neotropical migrants, and a nonsignificant decline for short-distance migrants over the 40 years. Further, abundances are not changing at different rates but occupancies are consistently greater over time for neotropical migrants versus permanent residents, likely driving the changes in A-O relationships observed.In these analyses, we documented changing A-O trends for different groups of species, over a relatively long time period for ecological studies, one of only a few studies to examine A-O relationships over time. Further, we have shown that a temporally unvarying abundance-occupancy relationship is not universal, and we posit that variability in A-O relationships is due to human impacts on habitats, coupled with variation in species' abilities to respond to human impacts.

Conserving the abundance of nonthreatened species

Human modification of the environment is driving declines in population size and distributional extent of much of the world's biota. These declines extend to many of the most abundant and widespread species, for which proportionally small declines can result in the loss of vast numbers of individuals, biomass, and interactions. These losses could have major localized effects on ecological and cultural processes and services without elevating a species' global extinction risk. Although most conservation effort is directed at species threatened with extinction in the very near term, the value of retaining abundance regardless of global extinction risk is justifiable based on many biodiversity or ecosystem service metrics, including cultural services, at scales from local to global. The challenges of identifying conservation priorities for widespread and abundant species include quantifying the effects of species' abundance on services and understanding how these effects are realized as populations decline. Negative effects of population declines may be disconnected from the threat processes driving declines because of species movements and environment flows (e.g., hydrology). Conservation prioritization for these species shares greater similarity with invasive species risk assessments than extinction risk assessments because of the importance of local context and per capita effects of abundance on other species. Because conservation priorities usually focus on preventing the extinction of threatened species, the rationale and objectives for incorporating declines of nonthreatened species must be clearly articulated, going beyond extinction risk to encompass the range of likely harmful effects (e.g., secondary extinctions, loss of ecosystem services) if declines persist or are not reversed. Research should focus on characterizing the effects of local declines in species that are not threatened globally across a range of ecosystem services and quantifying the spatial distribution of these effects through the distribution of abundance. The case for conserving abundance in nonthreatened species can be made most powerfully when the costs of losing this abundance are better understood.

Estimating species pools for a single ecological assemblage

BACKGROUND

The species pool concept was formulated over the past several decades and has since played an important role in explaining multi-scale ecological patterns. Previous statistical methods were developed to identify species pools based on broad-scale species range maps or community similarity computed from data collected from many areas. No statistical method is available for estimating species pools for a single local community (sampling area size may be very small as ≤ 1 km²). In this study, based on limited local abundance information, we developed a simple method to estimate the area size and richness of a species pool for a local ecological community. The method involves two steps. In the first step, parameters from a truncated negative trinomial model characterizing the distributional aggregation of all species (i.e., non-random species distribution) in the local community were estimated. In the second step, we assume that the unseen species in the local community are most likely the rare species, only found in the remaining part of the species pool, and vice versa, if the remaining portion of the pool was surveyed and was contrasted with the sampled area. Therefore, we can estimate the area size of the pool, as long as an abundance threshold for defining rare species is given. Since the size of the pool is dependent on the rarity threshold, to unanimously determine the pool size, we developed an optimal method to delineate the rarity threshold based on the balance of the changing rates of species absence probabilities in the sampled and unsampled areas of the pool.

RESULTS

For a 50 ha (0.5 km²) forest plot in the Barro Colorado Island of central Panama, our model predicted that the local, if not regional, species pool for the 0.5 km² forest plot was nearly the entire island. Accordingly, tree species richness in this pool was estimated as around 360. When the sampling size was smaller, the upper bound of the 95% confidence interval could reach 418, which was very close to the flora record of tree richness for the island. A numerical test further demonstrated the power and reliability of the proposed method, as the true values of area size and species richness for the hypothetical species pool have been well covered by the 95% confidence intervals of the true values.

CONCLUSIONS

Our method fills the knowledge gap on estimating species pools for a single local ecological assemblage with little information. The method is statistically robust and independent of sampling size, as proved by both empirical and numerical tests.

Abundance distributions for tree species in Great Britain: A two-stage approach to modeling abundance using species distribution modeling and random forest

High‐quality abundance data are expensive and time‐consuming to collect and often highly limited in availability. Nonetheless, accurate, high‐resolution abundance distributions are essential for many ecological applications ranging from species conservation to epidemiology. Producing models that can predict abundance well, with good resolution over large areas, has therefore been an important aim in ecology, but poses considerable challenges. We present a two‐stage approach to modeling abundance, combining two established techniques. First, we produce ensemble species distribution models (SDMs) of trees in Great Britain at a fine resolution, using much more common presence–absence data and key environmental variables. We then use random forest regression to predict abundance by linking the results of the SDMs to a much smaller amount of abundance data. We show that this method performs well in predicting the abundance of 20 of 25 tested British tree species, a group that is generally considered challenging for modeling distributions due to the strong influence of human activities. Maps of predicted tree abundance for the whole of Great Britain are provided at 1 km² resolution. Abundance maps have a far wider variety of applications than presence‐only maps, and these maps should allow improvements to aspects of woodland management and conservation including analysis of habitats and ecosystem functioning, epidemiology, and disease management, providing a useful contribution to the protection of British trees. We also provide complete R scripts to facilitate application of the approach to other scenarios.

The efficacy of monitoring techniques for detecting small mammals and reptiles in arid environments

Context Accurate surveying and monitoring of biodiversity provides essential baseline data for developing and implementing effective environmental management strategies. Land managers in arid zones face the challenge of managing vast, remote landscapes that support numerous cryptic species that are difficult to detect and monitor. Although researchers and land managers are using an increasingly wider variety of monitoring techniques to detect and monitor species, little is known of the relative effectiveness and comparative costs of these techniques. Aims The present study simultaneously assessed the efficacy of three popular monitoring techniques utilised in the spinifex sand plains of arid Australia, namely, live trapping, sign surveys and passive infrared (PIR)-camera trapping. Methods We explored variations in capture rates and species richness for each technique and compared initial and on-going costs of the techniques over time. Key results Sign surveys detected the greatest number of species and groups overall. Detectability of small mammals and reptiles, as a target group, was greater using PIR cameras, although the probability of detection by each technique varied among specific species. PIR cameras were initially the most expensive technique; however, the low ongoing costs of maintaining cameras in the field meant that they became the most cost effective after eight survey periods. Conclusions Each of the techniques tested here showed biases towards the detection of specific groups or species in the spinifex sand-plain habitat of Australia. Regardless, PIR cameras performed better at detecting the greatest diversity of target species and financially over time. Implications To accurately survey species across vast areas and climate variations, studies often extend over long time periods. Many long-term studies would be likely to benefit financially from the increased deployment of PIR cameras alongside or in place of live trapping surveys, with little impact on the ability to monitor the presence of most species in the region.

Comparative analysis of abundance–occupancy relationships for species at risk at both broad taxonomic and spatial scales

The abundance–occupancy relationship is one of the most well-examined relationships in ecology. At the species level, a positive association has been widely documented. However, until recently, research on the nature of this relationship at broad taxonomic and spatial scales has been limited. Here, we perform a comparative analysis of 12 taxonomic groups across a large spatial scale (Canada), using data on Canadian species at risk: amphibians, arthropods, birds, freshwater fishes, lichens, marine fishes, marine mammals, molluscs, mosses, reptiles, terrestrial mammals, and vascular plants. We find a significantly positive relationship in all taxonomic groups with the exception of freshwater fishes (negative association) and lichens (no association). In general, our work underscores the strength and breadth of this apparently fundamental relationship and provides insight into novel applications for large-scale population dynamics. Further development of species-independent abundance–occupancy relationships, or those of a similar nature, might well prove instrumental in serving as starting points for developing species-independent reference points and recovery strategies.

Spatially-explicit estimation of geographical representation in large-scale species distribution datasets

Much ecological research relies on existing multispecies distribution datasets. Such datasets, however, can vary considerably in quality, extent, resolution or taxonomic coverage. We provide a framework for a spatially-explicit evaluation of geographical representation within large-scale species distribution datasets, using the comparison of an occurrence atlas with a range atlas dataset as a working example. Specifically, we compared occurrence maps for 3773 taxa from the widely-used Atlas Florae Europaeae (AFE) with digitised range maps for 2049 taxa of the lesser-known Atlas of North European Vascular Plants. We calculated the level of agreement at a 50-km spatial resolution using average latitudinal and longitudinal species range, and area of occupancy. Agreement in species distribution was calculated and mapped using Jaccard similarity index and a reduced major axis (RMA) regression analysis of species richness between the entire atlases (5221 taxa in total) and between co-occurring species (601 taxa). We found no difference in distribution ranges or in the area of occupancy frequency distribution, indicating that atlases were sufficiently overlapping for a valid comparison. The similarity index map showed high levels of agreement for central, western, and northern Europe. The RMA regression confirmed that geographical representation of AFE was low in areas with a sparse data recording history (e.g., Russia, Belarus and the Ukraine). For co-occurring species in south-eastern Europe, however, the Atlas of North European Vascular Plants showed remarkably higher richness estimations. Geographical representation of atlas data can be much more heterogeneous than often assumed. Level of agreement between datasets can be used to evaluate geographical representation within datasets. Merging atlases into a single dataset is worthwhile in spite of methodological differences, and helps to fill gaps in our knowledge of species distribution ranges. Species distribution dataset mergers, such as the one exemplified here, can serve as a baseline towards comprehensive species distribution datasets.

Downscaling species occupancy from coarse spatial scales

The measurement and prediction of species' populations at different spatial scales is crucial to spatial ecology as well as conservation biology. An efficient yet challenging goal to achieve such population estimates consists of recording empirical species' presence and absence at a specific regional scale and then trying to predict occupancies at finer scales. So far the majority of the methods have been based on particular species' distributional features deemed to be crucial for downscaling occupancy. However, only a minority of them have dealt explicitly with specific spatial features. Here we employ a wide class of spatial point processes, the shot noise Cox processes (SNCP), to model species occupancies at different spatial scales and show that species' spatial aggregation is crucial for predicting population estimates at fine scales starting from coarser ones. These models are formulated in continuous space and locate points regardless of the arbitrary resolution that one employs to study the spatial pattern. We compare the performances of nine models, calibrated at regional scales and demonstrate that a very simple class of SNCP, the Thomas process, is able to outperform other published models in predicting occupancies down to areas four orders of magnitude smaller than the ones employed for the parameterization. We conclude by explaining the ability of the approach to infer spatially explicit information from spatially implicit measures, the potential of the framework to combine niche and spatial models, and the possibility of reversing the method to allow upscaling.

A point-process model for variance-occupancy-abundance relationships

The relationship between species abundance, the variance of the number of individuals, and species occupancy is a fundamental ecological characteristic of a community. Moreover, this relationship varies across scales, and any model for the variance-occupancy-abundance (VOA) relationship has to address its scale dependency in a consistent way. In this study, point-process theory was used to define a multiscale model that jointly predicts the VOA relationship across scales in a consistent way. This provides a tool to jointly analyze data sets collected at different scales and to give insights into the biological processes underlying the VOA relationship. This model can also account for different types of individual spatial pattern (clustered, random, or regular). Three stand-mapping data sets of tree species in tropical rain forests were used to assess the relevance of this model. When compared with four existing models, the model based on point-process theory provided the best fit to the data and was the most often ranked as the model with the best predictive performance.

Linking biodiversity patterns by autocorrelated random sampling

Biodiversity macroecology deals with the commonly measured variables of abundance, distribution, occupancy, and range size across two scales: the local (or α) and regional (γ). There are ca. 15 patterns consisting of the frequency distributions of the variables, variables as a function of area or sample size, and interrelationships between variables that appear to be very general if not close to universal. A number of links can be drawn between these patterns. In particular, I show that local communities can be seen as random samples of the regional pool, but only as a special form of sampling that is autocorrelated due to the spatial clumping of individuals within a species. I describe two distinct sets of mathematical machinery that can start with the regional species abundance distribution and then predict local species richness, local species abundance distributions, and β-diversity (in the form of species area relationships or decay of similarity with distance). I conclude by examining some of the implications of the fact that biodiversity patterns are linked by autocorrelated sampling.