Estimating abundance of unmarked animal populations: accounting for imperfect detection and other sources of zero inflation

Availability of alternative prey rather than intraguild interactions determines the local abundance of two understudied and threatened small carnivore species

Intraguild interactions influence the structure and local dynamics of carnivore mammals' assemblages. The potential effects of these interactions are often determined by the body size of competing members and may result in negative relationships in their abundance and, ultimately, lead to species exclusion or coexistence. The relative importance of interspecific interactions along with landscape characteristics in determining population patterns of understudied and threatened sympatric small carnivores, such as skunks, remains poorly documented. Therefore, we assessed the spatiotemporal variation in the abundance of American hog-nosed skunks Conepatus leuconotus and pygmy spotted skunks Spilogale pygmaea and the effect of interspecific interactions, resource availability, and habitat complexity on their local abundance in areas with the deciduous tropical forest south of the Mexican Pacific slope. We used presence-absence data for skunk species from three camera-trapping surveys between 2018 and 2020 in combination with Royle-Nichols occupancy models fitted in a Bayesian framework to estimate abundance, incorporating the effects of covariates related to the factors evaluated. We analyzed the relationship between the abundances of skunks using Bayesian Generalized Linear Models. Both skunk species showed significant differences in their abundances between seasons and between study sites. Overall, pygmy skunks were more abundant than hog-nosed skunks. We found negative relationships among the relative abundances of skunks during the dry seasons, but no evidence that local abundance is governed by the competitive dominance of the larger species. Patterns of skunk abundance were better explained by prey availability and other predictors related to habitat complexity, rather than interspecific interactions, since these models showed the highest predictive accuracies and strong positive and negative relationships. Our study highlights the underlying factors that determine the local abundance of these understudied and threatened small carnivores, allowing us to better understand the mechanisms that govern their coexistence for effective management and conservation of species in seasonal environments.

Using convolutional neural networks to count parrot nest-entrances on photographs from the largest known colony of Psittaciformes

Counting animal populations is fundamental to understand ecological processes. Counts make it possible to estimate the size of an animal population at specific points in time, which is essential information for understanding demographic change. However, in the case of large populations, counts are time-consuming, particularly if carried out manually. Here, we took advantage of convolutional neural networks (CNN) to count the total number of nest-entrances in 222 photographs covering the largest known Psittaciformes (Aves) colony in the world. We conducted our study at the largest Burrowing Parrot Cyanoliseus patagonus colony, located on a cliff facing the Atlantic Ocean in the vicinity of El Cóndor village, in north-eastern Patagonia, Argentina. We also aimed to investigate the distribution of nest-entrances along the cliff with the colony. For this, we used three CNN architectures, U-Net, ResUnet, and DeepLabv3. The U-Net architecture showed the best performance, counting a mean of 59,842 Burrowing Parrot nest-entrances across the colony, with a mean absolute error of 2.7 nest-entrances over the testing patches, measured as the difference between actual and predicted counts per patch. Compared to a previous study conducted at El Cóndor colony more than 20 years ago, the CNN architectures also detected noteworthy differences in the distribution of the nest-entrances along the cliff. We show that the strong changes observed in the distribution of nest-entrances are a measurable effect of a long record of human-induced disturbance to the Burrowing Parrot colony at El Cóndor. Given the paramount importance of the Burrowing Parrot colony at El Cóndor, which concentrates 71% of the world's population of this species, we advocate that it is imperative to reduce such a degree of disturbance before the parrots reach the limit of their capacity of adaptation.

Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects

Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects-from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

When and where? Day-night alterations in wild boar space use captured by a generalized additive mixed model

Wild boar (Sus scrofa), an abundant species across Europe, is often subjected to management in agro-ecosystems in order to control population size, or to scare them away from agricultural fields to safeguard crop yields. Wild boar management can benefit from a better understanding on changes in its space use across the diel cycle (i.e., diel space use) in relation to variable hunting pressures or other factors. Here, we estimate wild boar diel space use in an agro-ecosystem in central Belgium during four consecutive "growing seasons" (i.e., April-September). To achieve this, we fit generalized additive mixed models (GAMMs) to camera trap data of wild boar aggregated over 1-h periods. Our results reveal that wild boar are predominantly nocturnal in all of the hunting management zones in Meerdaal, with activity peaks around sunrise and sunset. Hunting events in our study area tend to take place around sunrise and sunset, while non-lethal human activities occur during sunlight hours. Our GAMM reveals that wild boar use different areas throughout the diel cycle. During the day, wild boar utilized areas in the centre of the forest, possibly to avoid human activities during daytime. During the night, they foraged near (or in) agricultural fields. A post hoc comparison of space use maps of wild boar in Meerdaal revealed that their diurnal and nocturnal space use were uncorrelated. We did not find sufficient evidence to prove that wild boar spatiotemporally avoid hunters. Finally, our work reveals the potential of GAMMs to model variation in space across 24-h periods from camera trap data, an application that will be useful to address a range of ecological questions. However, to test the robustness of this approach we advise that it should be compared against telemetry-based methods to derive diel space use.

Human-Wildlife Conflict Mitigation Based on Damage, Distribution, and Activity: A Case Study of Wild Boar in Zhejiang, Eastern China

Human-wildlife conflicts are becoming increasingly common worldwide and are a challenge to biodiversity management. Compared with compensatory management, which often focuses on solving emergency conflicts, mitigation management allows decision-makers to better understand where the damage is distributed, how the species are distributed and when the species conduct their activity. Here, we integrated data collected from 90 districts/counties' damage surveys and 1271 camera traps to understand the damage status, abundance, density and activity rhythms of wild boar (Sus scrofa) in Zhejiang, Eastern China, from January 2019 to August 2023. We found that (1) wild boar-human conflicts were mainly distributed in the northwest and southwest mountainous regions of Zhejiang Province; (2) the total abundance of wild boar was 115,156 ± 24,072 individuals, indicating a growing trend over the past decade and a higher density in the western and southern regions; (3) wild boar exhibited different activity patterns across different damage regions, and the periods around 7:00, 11:00 and 16:00 represented activity peaks for wild boar in seriously damaged regions. The damage distribution, density, distribution and activity rhythms provide specific priority regions and activity intensity peaks for conflict mitigation. We believe that these findings based on the damage, distribution and activity could provide a scientific basis for mitigation management at the county level and enrich the framework of human-wildlife conflict mitigation.

N-mixture models for population estimation: Application in spotted lanternfly egg mass survey

Population density and structure are critical to nature conservation and pest management. Traditional sampling methods such as capture-mark-recapture and catch-effort can't be used in situations where catching, marking, or removing individuals are not feasible. N-mixture models use repeated count data to estimate population abundance based on detection probability. They are widely adopted in wildlife surveys in recent years to account for imperfect detection. However, its application in entomology is relatively new. In this paper, we describe the general procedures of N-mixture models in population studies from data collection to model fitting and evaluation. Using Lycorma delicatula egg mass survey data at 28 plots in seven sites from the field, we found that detection probability (p) was negatively correlated with tree diameter at breast height (DBH), ranged from 0.516 [95 % CI: 0.470-0.561] to 0.614 [95 % CI: 0.566-0.660] between the 1st and the 3rd sample period. Furthermore, egg mass abundance (λ) was positively associated with basal area (BA) for the sample unit (single tree), with more egg masses on tree of heaven (TOH) trees. More egg masses were also expected on trees of other species in TOH plots. Predicted egg mass density (masses/100 m2) ranged from 5.0 (95 % CI: 3.0-16.0) (Gordon) to 276.9 (95 % CI: 255.0-303.0) (Susquehannock) for TOH plots, and 11.0 (95 % CI: 9.00-15.33) (Gordon) to 228.3 (95 % CI: 209.7-248.3) (Burlington) for nonTOH plots. Site-specific abundance estimates from N-mixture models were generally higher compared to observed maximum counts. N-mixture models could have great potential in insect population surveys in agriculture and forestry in the future.

Simulation-based assessment of the performance of hierarchical abundance estimators for camera trap surveys of unmarked species

Knowledge on animal abundances is essential in ecology, but is complicated by low detectability of many species. This has led to a widespread use of hierarchical models (HMs) for species abundance, which are also commonly applied in the context of nature areas studied by camera traps (CTs). However, the best choice among these models is unclear, particularly based on how they perform in the face of complicating features of realistic populations, including: movements relative to sites, multiple detections of unmarked individuals within a single survey, and low detectability. We conducted a simulation-based comparison of three HMs (Royle-Nichols, binomial N-mixture and Poisson N-mixture model) by generating groups of unmarked individuals moving according to a bivariate Ornstein-Uhlenbeck process, monitored by CTs. Under a range of simulated scenarios, none of the HMs consistently yielded accurate abundances. Yet, the Poisson N-mixture model performed well when animals did move across sites, despite accidental double counting of individuals. Absolute abundances were better captured by Royle-Nichols and Poisson N-mixture models, while a binomial N-mixture model better estimated the actual number of individuals that used a site. The best performance of all HMs was observed when estimating relative trends in abundance, which were captured with similar accuracy across these models.

Camera Trap Methods and Drone Thermal Surveillance Provide Reliable, Comparable Density Estimates of Large, Free-Ranging Ungulates

Camera traps and drone surveys both leverage advancing technologies to study dynamic wildlife populations with little disturbance. Both techniques entail strengths and weaknesses, and common camera trap methods can be confounded by unrealistic assumptions and prerequisite conditions. We compared three methods to estimate the population density of white-tailed deer (Odocoileus virgnianus) in a section of Pilot Mountain State Park, NC, USA: (1) camera trapping using mark-resight ratios or (2) N-mixture modeling and (3) aerial thermal videography from a drone platform. All three methods yielded similar density estimates, suggesting that they converged on an accurate estimate. We also included environmental covariates in the N-mixture modeling to explore spatial habitat use, and we fit models for each season to understand temporal changes in population density. Deer occurred in greater densities on warmer, south-facing slopes in the autumn and winter and on cooler north-facing slopes and in areas with flatter terrain in the summer. Seasonal density estimates over two years suggested an annual cycle of higher densities in autumn and winter than in summer, indicating that the region may function as a refuge during the hunting season.

Wildlife Population Assessment: Changing Priorities Driven by Technological Advances

Advances in technology are having a large effect on the priorities for innovation in statistical ecology. Collaborations between statisticians and ecologists have always been important in driving methodological development, but increasingly, expertise from computer scientists and engineers is also needed. We discuss changes that are occurring and that may occur in the future in surveys for estimating animal abundance. As technology advances, we expect classical distance sampling and capture-recapture to decrease in importance, as camera (still and video) survey, acoustic survey, spatial capture-recapture and genetic methods continue to develop and find new applications. We explore how these changes are impacting the work of the statistical ecologist.

Finite mixtures of mean-parameterized Conway-Maxwell-Poisson models

For modeling count data, the Conway-Maxwell-Poisson (CMP) distribution is a popular generalization of the Poisson distribution due to its ability to characterize data over- or under-dispersion. While the classic parameterization of the CMP has been well-studied, its main drawback is that it is does not directly model the mean of the counts. This is mitigated by using a mean-parameterized version of the CMP distribution. In this work, we are concerned with the setting where count data may be comprised of subpopulations, each possibly having varying degrees of data dispersion. Thus, we propose a finite mixture of mean-parameterized CMP distributions. An EM algorithm is constructed to perform maximum likelihood estimation of the model, while bootstrapping is employed to obtain estimated standard errors. A simulation study is used to demonstrate the flexibility of the proposed mixture model relative to mixtures of Poissons and mixtures of negative binomials. An analysis of dog mortality data is presented.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00362-023-01452-x.

A Deep-Learning Based Pipeline for Estimating the Abundance and Size of Aquatic Organisms in an Unconstrained Underwater Environment from Continuously Captured Stereo Video

The utilization of stationary underwater cameras is a modern and well-adapted approach to provide a continuous and cost-effective long-term solution to monitor underwater habitats of particular interest. A common goal of such monitoring systems is to gain better insight into the dynamics and condition of populations of various marine organisms, such as migratory or commercially relevant fish taxa. This paper describes a complete processing pipeline to automatically determine the abundance, type and estimate the size of biological taxa from stereoscopic video data captured by the stereo camera of a stationary Underwater Fish Observatory (UFO). A calibration of the recording system was carried out in situ and, afterward, validated using the synchronously recorded sonar data. The video data were recorded continuously for nearly one year in the Kiel Fjord, an inlet of the Baltic Sea in northern Germany. It shows underwater organisms in their natural behavior, as passive low-light cameras were used instead of active lighting to dampen attraction effects and allow for the least invasive recording possible. The recorded raw data are pre-filtered by an adaptive background estimation to extract sequences with activity, which are then processed by a deep detection network, i.e., Yolov5. This provides the location and type of organisms detected in each video frame of both cameras, which are used to calculate stereo correspondences following a basic matching scheme. In a subsequent step, the size and distance of the depicted organisms are approximated using the corner coordinates of the matched bounding boxes. The Yolov5 model employed in this study was trained on a novel dataset comprising 73,144 images and 92,899 bounding box annotations for 10 categories of marine animals. The model achieved a mean detection accuracy of 92.4%, a mean average precision (mAP) of 94.8% and an F1 score of 93%.

Spatially prioritizing mitigation for amphibian roadkills based on fatality estimation and landscape conversion

Roads cause biodiversity loss and the effects of wildlife-vehicle collisions may ripple from individuals and populations to ecosystem functioning. Amphibians are threatened worldwide and, despite being particularly prone to roadkill impacts, they are often neglected in assessments. Here, we develop a sampling and analytical framework for spatially prioritizing mitigation actions for anuran amphibian roadkills based on fatality estimation and landscape conversion. The framework is composed of the six following steps: (1) pre-selection of segments to survey using the wetland coverage in the surroundings and the presence of roadkills of aquatic reptiles as a proxy for wet areas; (2) spatiotemporally replicated counts with a dependent double-observer protocol, that is, each segment is sampled multiple times by two pairs of people on foot; (3) extraction of covariates hypothesized to affect spatial and temporal variation in roadkill rates and persistence; (4) hierarchical open-population N-mixture modelling to estimate population dynamics parameters, which accounts for imperfect detection and spatiotemporal heterogeneity in removal, detection, and roadkill rates, and explicitly estimates carcass entries per time interval. (5) Assessment of land cover transition to infer landscape stability; and (6) prioritization of segments based on higher fatality rates and lower landscape conversion rates. We estimated a mean of 136 (95%CrI = 130–142) anurans roadkill per km per day in the 50 sample sites selected. The initial number of carcasses had a positive relationship with the percentage occupied by wetlands and a negative association with the percentage occupied by urban areas. The number of entrant carcass per interval was higher in the presence of rainfall and had a positive association with the wetlands cover. Carcass persistence probability was higher at night and lower in sites with high traffic volume. Ten segments (~1% of road extension) were prioritized using the median as threshold for fatality estimates and landscape conversion. It is urgent to appropriately evaluate the number of amphibians roadkilled aiming to plan and implement mitigation measures specifically designed for these small animals. Our approach accounts for feasibility (focused on sites with greater relevance), robustness (considering imperfect detection), and steadiness (less prone to loss of effectiveness due to landscape dynamics).

Spatial and temporal non-stationarity in long-term population dynamics of over-wintering birds of North America

Understanding population changes across long time scales and at fine spatiotemporal resolutions is important for confronting a broad suite of conservation challenges. However, this task is hampered by a lack of quality long-term census data for multiple species collected across large geographic regions. Here, we used century-long (1919-2018) data from the Audubon Christmas Bird Count (CBC) survey to assess population changes in over 300 avian species in North America and evaluate their temporal non-stationarity. To estimate population sizes across the entire century, we employed a Bayesian hierarchical model that accounts for species detection probabilities, variable sampling effort, and missing data. We evaluated population trends using generalized additive models (GAMs) and assessed temporal non-stationarity in the rate of population change by extracting the first derivatives from the fitted GAM functions. We then summarized the population dynamics across species, space, and time using a non-parametric clustering algorithm that categorized individual population trends into four distinct trend clusters. We found that species varied widely in their population trajectories, with over 90% of species showing a considerable degree of spatial and/or temporal non-stationarity, and many showing strong shifts in the direction and magnitude of population trends throughout the past century. Species were roughly equally distributed across the four clusters of population trajectories, although grassland, forest, and desert specialists more commonly showed declining trends. Interestingly, for many species, region-wide population trends often differed from those observed at individual sites, suggesting that conservation decisions need to be tailored to fine spatial scales. Together, our results highlight the importance of considering spatial and temporal non-stationarity when assessing long-term population changes. More generally, we demonstrate the promise of novel statistical techniques for improving the utility and extending the temporal scope of existing citizen science datasets.

N-mixture model-based estimate of relative abundance of sloth bear (Melursus ursinus) in response to biotic and abiotic factors in a human-dominated landscape of central India

Reliable estimation of abundance is a prerequisite for a species' conservation planning in human-dominated landscapes, especially if the species is elusive and involved in conflicts. As a means of population estimation, the importance of camera traps has been recognized globally, although estimating the abundance of unmarked, cryptic species has always been a challenge to conservation biologists. This study explores the use of the N-mixture model with three probability distributions, i.e., Poisson, negative binomial (NB) and zero-inflated Poisson (ZIP), to estimate the relative abundance of sloth bears (Melursus ursinus) based on a camera trapping exercise in Sanjay Tiger Reserve, Madhya Pradesh from December 2016 to April 2017. We used environmental and anthropogenic covariates to model the variation in the abundance of sloth bears. We also compared null model estimates (mean site abundance) obtained from the N-mixture model to those of the Royle-Nichols abundance-induced heterogeneity model (RN model) to assess the application of similar site-structured models. Models with Poisson distributions produced ecologically realistic and more precise estimates of mean site abundance (λ = 2.60 ± 0.64) compared with other distributions, despite the relatively high Akaike Information Criterion value. Area of mixed and sal forest, the photographic capture rate of humans and distance to the nearest village predicted a higher relative abundance of sloth bears. Mean site abundance estimates of sloth bears obtained from the N-mixture model (Poisson distribution) and the RN model were comparable, indicating the overall utility of these models in this field. However, density estimates of sloth bears based on spatially explicit methods are essential for evaluating the efficacy of the relatively more cost-effective N-mixture model. Compared to commonly used index/encounter-based methods, the N-mixture model equipped with knowledge on governing biotic and abiotic factors provides better relative abundance estimates for a species like the sloth bear. In the absence of absolute abundance estimates, the present study could be insightful for the long-term conservation and management of sloth bears.

Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset

To analyze species count data when detection is imperfect, ecologists need models to estimate relative abundance in the presence of unknown sources of heterogeneity. Two candidate models are generalized linear mixed models (GLMMs) and hierarchical N-mixture models. GLMMs are computationally robust but do not explicitly separate detection from abundance patterns. N-mixture models separately estimate detection and abundance via a latent state but are sensitive to violations in assumptions and subject to practical estimation issues. When one can assume that detection is not systematically confounded with ecological patterns of interest, these two models can be viewed as sharing a heuristic framework for relative abundance estimation. Model selection can then determine which predicts observed counts best, for example by AIC. We compared four N-mixture model variants and two GLMM variants for predicting bird counts in local subsets of a citizen science dataset, eBird, based on model selection and goodness-of-fit measures. We found that both GLMMs and N-mixture models—especially N-mixtures with beta-binomial detection submodels—were supported in a moderate number of datasets, suggesting that both tools are useful and that relative fit is context-dependent. We provide faster software implementations of N-mixture likelihood calculations and a reparameterization to interpret unstable estimates for N-mixture models.

Evaluating sources of bias in pedigree-based estimates of breeding population size

Applications of genetic-based estimates of population size are expanding, especially for species for which traditional demographic estimation methods are intractable due to the rarity of adult encounters. Estimates of breeding population size (N_S ) are particularly amenable to genetic-based approaches as the parameter can be estimated using pedigrees reconstructed from genetic data gathered from discrete juvenile cohorts, therefore eliminating the need to sample adults in the population. However, a critical evaluation of how genotyping and sampling effort influence bias in pedigree reconstruction, and how these biases subsequently influence estimates of N_S , is needed to evaluate the efficacy of the approach under a range of scenarios. We simulated a model system to understand the interactive effects of genotyping and sampling effort on error in genetic pedigrees reconstructed from the program COLONY. We then evaluated how errors in pedigree reconstruction influenced bias and precision in estimates of N_S using three different rarefaction estimators. Results indicated that pedigree error can be minimal when adequate genetic data are available, such as when juvenile sample sizes are large and/or individuals are genotyped at many informative loci. However, even in cases for which data are limited, using results of the simulation analysis to understand the magnitude and sources of bias in reconstructed pedigrees can still be informative when estimating N_S . We applied results of the simulation analysis to evaluate N ̂ $$ \hat{N} $$ _S for a population of federally endangered Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) in the Delaware River, USA. Our results indicated that N_S is likely to be three orders of magnitude lower compared with historic breeding population sizes, which is a considerable advancement in our understanding of the population status of Atlantic sturgeon in the Delaware River. Our analyses are broadly applicable in the design and interpretation of studies seeking to estimate N_S and can help to guide conservation decisions when ecological uncertainty is high. The utility of these results is expected to grow as rapid advances in genetic technologies increase the popularity of genetic population monitoring and estimation.

Quantitative selection of focal birds and mammals in higher-tier risk assessment: An application to rice cultivations

European Pesticide Registration requires a risk assessment (RA) for nontarget organisms according to EU Regulation. European Authorities have developed Guidance Documents (GDs) for RA considering exposure scenarios for the required organisms typical for terrestrial crops. The "Birds and Mammals EFSA GD" allows using multiple sources of information to extract information on species frequency needed in identifying focal species for higher-tier RA. We developed an analytical framework to calculate species frequency according to availability of species and habitat quantitative data. Since the exposure scenarios reported in the EFSA GD are inconsistent for rice, we tested the method on birds and mammals in a portion of the largest rice-cultivated area of Europe, the Italian Po floodplain. We derived three lists of focal species: (a) an expert-based list based on land-use data only, which can be useful for a preliminary exploration of potential candidate species; (b) a list derived from the interpolation of species data only, which reflects actual species frequency in rice fields; and (c) a list obtained by a species distribution model based on species monitoring and land-use data, which account for species selectivity for rice crops and are transferable to other contexts. Focal species were identified for crop-specific diet-foraging guilds, to build specific exposure scenarios to assess the risk from pesticides application in rice fields. The partial differences between our lists and those previously proposed highlight the need for identifying national lists, which can vary according to study area, biogeographic region and exposure scenarios. The application of the proposed method in European rice-producing countries should lead to crop-specific lists, which could then be integrated to obtain a flexible European list applicable to higher-tier RA. Integr Environ Assess Manag 2022;18:1020-1034. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Accounting for detection probability with overestimation by integrating double monitoring programs over 40 years

In the context of wildlife population declines, increasing computer power over the last 20 years allowed wildlife managers to apply advanced statistical techniques that has improved population size estimates. However, respecting the assumptions of the models that consider the probability of detection, such as N-mixture models, requires the implementation of a rigorous monitoring protocol with several replicate survey occasions and no double counting that are hardly adaptable to field conditions. When the logistical, economic and ecological constraints are too strong to meet model assumptions, it may be possible to combine data from independent surveys into the modelling framework in order to understand population dynamics more reliably. Here, we present a state-space model with an error process modelled on the log scale to evaluate wintering waterfowl numbers in the Camargue, southern France, while taking a conditional probability of detection into consideration. Conditional probability of detection corresponds to estimation of a detection probability index, which is not a true probability of detection, but rather conditional on the difference to a particular baseline. The large number of sites (wetlands within the Camargue delta) and years monitored (44) provide significant information to combine both terrestrial and aerial surveys (which constituted spatially and temporally replicated counts) to estimate a conditional probability of detection, while accounting for false-positive counting errors and changes in observers over the study period. The model estimates abundance indices of wintering Common Teal, Mallard and Common Coot, all species abundant in the area. We found that raw counts were underestimated compared to the predicted population size. The model-based data integration approach as described here seems like a promising solution that takes advantage of as much as possible of the data collected from several methods when the logistic constraints do not allow the implementation of a permanent monitoring and analysis protocol that takes into account the detectability of individuals.

N-mixture models provide informative crocodile (Crocodylus moreletii) abundance estimates in dynamic environments

Estimates of animal abundance provide essential information for population ecological studies. However, the recording of individuals in the field can be challenging, and accurate estimates require analytical techniques which account for imperfect detection. Here, we quantify local abundances and overall population size of Morelet's crocodiles (Crocodylus moreletii) in the region of Calakmul (Campeche, Mexico), comparing traditional approaches for crocodylians (Minimum Population Size-MPS; King's Visible Fraction Method-VFM) with binomial N-mixture models based on Poisson, zero-inflated Poisson (ZIP) and negative binomial (NB) distributions. A total of 191 nocturnal spotlight surveys were conducted across 40 representative locations (hydrologically highly dynamic aquatic sites locally known as aguadas) over a period of 3 years (2017-2019). Local abundance estimates revealed a median of 1 both through MPS (min-max: 0-89; first and third quartiles, Q₁-Q₃: 0-7) and VFM (0-112; Q₁-Q₃: 0-9) non-hatchling C. moreletii for each aguada, respectively. The ZIP based N-mixture approach shown overall superior confidence over Poisson and NB, and revealed a median of 6 ± 3 individuals (min = 0; max = 120 ± 18; Q₁ = 0; Q₃ = 18 ± 4) jointly with higher detectabilities in drying aguadas with low and intermediate vegetation cover. Extrapolating these inferences across all waterbodies in the study area yielded an estimated ~10,000 (7,000-11,000) C. moreletii present, highlighting Calakmul as an important region for this species. Because covariates enable insights into population responses to local environmental conditions, N-mixture models applied to spotlight count data result in particularly insightful estimates of crocodylian detection and abundance.

Climate drives long-term change in Antarctic Silverfish along the western Antarctic Peninsula

Over the last half of the 20^th century, the western Antarctic Peninsula has been one of the most rapidly warming regions on Earth, leading to substantial reductions in regional sea ice coverage. These changes are modulated by atmospheric forcing, including the Amundsen Sea Low (ASL) pressure system. We utilized a novel 25-year (1993-2017) time series to model the effects of environmental variability on larvae of a keystone species, the Antarctic Silverfish (Pleuragramma antarctica). Antarctic Silverfish use sea ice as spawning habitat and are important prey for penguins and other predators. We show that warmer sea surface temperature and decreased sea ice are associated with reduced larval abundance. Variability in the ASL modulates both sea surface temperature and sea ice; a strong ASL is associated with reduced larvae. These findings support a narrow sea ice and temperature tolerance for adult and larval fish. Further regional warming predicted to occur during the 21st century could displace populations of Antarctic Silverfish, altering this pelagic ecosystem.

Spatiotemporal variation in the adult sex ratio, male aggregation, and movement of two tropical cloud forest dung beetles

While theory suggests that at conception the sex ratio should be balanced (1:1), this can be variable across space and time in wild populations. Currently, studies of the environmental factors that regulate adult sex ratio (ASR) in species with different life history traits are scarce. Using capture-recapture over a year, we analyzed the influence of habitat type (forest and nonforest) and season (rainy and dry) on variation in ASR, male aggregation and the trajectory movement of 2 dung beetle species with different life history traits: Deltochilum mexicanum (a hornless roller species) and Dichotomius satanas (a tunneler species with horns on its head and thorax). We found opposite tendencies. The D. mexicanum population tends to be female-biased, but the population of D. satanas tends to be predominantly male, and observed values were not related to habitat type or season. However, the 95% confidence intervals estimated were highly variable between seasons depending on habitat. On examining the monthly variation in ASR for both habitats, we found that it depends on the species. In addition, male aggregation differed between species depending on habitat type and season, and species movement patterns were closely related to their habitat preferences. Based on our results, we argue that comparative population studies of species with different life history traits are necessary to understand the variation in demographic parameters as well as its ecological and evolutionary implications in the face of spatial and climatic environmental variation.

Species Traits Drive Long-Term Population Trends of Common Breeding Birds in Northern Italy

Long-term population trends are considerable sources of information to set wildlife conservation priorities and to evaluate the performance of management actions. In addition, trends observed in functional groups (e.g., trophic guilds) can provide the foundation to test specific hypotheses about the drivers of the observed population dynamics. The aims of this study were to assess population trends of breeding birds in Lombardy (N Italy) from 1992 to 2019 and to explore the relationships between trends and species sharing similar ecological and life history traits. Trends were quantified and tested for significance by weighted linear regression models and using yearly population indices (median and 95% confidence interval) predicted through generalized additive models. Results showed that 45% of the species increased, 24% decreased, and 31% showed non-significant trends. Life history traits analyses revealed a general decrease of migrants, of species with short incubation period and of species with high annual fecundity. Ecological traits analyses showed that plant-eaters and species feeding on invertebrates, farmland birds, and ground-nesters declined, while woodland birds increased. Further studies should focus on investigation of the relationship between long-term trends and species traits at large spatial scales, and on quantifying the effects of specific drivers across multiple functional groups.

Why Are Species’ Traits Weak Predictors of Range Shifts?

We examine the evidence linking species’ traits to contemporary range shifts and find they are poor predictors of range shifts that have occurred over decades to a century. We then discuss reasons for the poor performance of traits for describing interspecific variation in range shifts from two perspectives: ( a) factors associated with species’ traits that degrade range-shift signals stemming from the measures used for species’ traits, traits that are typically not analyzed, and the influence of phylogeny on range-shift potential and ( b) issues in quantifying range shifts and relating them to species’ traits due to imperfect detection of species, differences in the responses of altitudinal and latitudinal ranges, and emphasis on testing linear relationships between traits and range shifts instead of nonlinear responses. Improving trait-based approaches requires a recognition that traits within individuals interact in unexpected ways and that different combinations of traits may be functionally equivalent.

Evaluation of camera trap-based abundance estimators for unmarked populations

Estimates of species abundance are critical to understand population processes and to assess and select management actions. However, capturing and marking individuals for abundance estimation, while providing robust information, can be economically and logistically prohibitive, particularly for species with cryptic behavior. Camera traps can be used to collect data at temporal and spatial scales necessary for estimating abundance, but the use of camera traps comes with limitations when target species are not uniquely identifiable (i.e., "unmarked"). Abundance estimation is particularly useful in the management of invasive species, with herpetofauna being recognized as some of the most pervasive and detrimental invasive vertebrate species. However, the use of camera traps for these taxa presents additional challenges with relevancy across multiple taxa. It is often necessary to use lures to attract animals in order to obtain sufficient observations, yet lure attraction can influence species' landscape use and potentially induce bias in abundance estimators. We investigated these challenges and assessed the feasibility of obtaining reliable abundance estimates using camera-trapping data on a population of invasive brown treesnakes (Boiga irregularis) in Guam. Data were collected using camera traps in an enclosed area where snakes were subject to high-intensity capture-recapture effort, resulting in presumed abundance of 116 snakes (density = 23/ha). We then applied spatial count, random encounter and staying time, space to event, and instantaneous sampling estimators to photo-capture data to estimate abundance and compared estimates to our presumed abundance. We found that all estimators for unmarked populations performed poorly, with inaccurate or imprecise abundance estimates that limit their usefulness for management in this system. We further investigated the sensitivity of these estimators to the use of lures (i.e., violating the assumption that animal behavior is unchanged by sampling) and camera density in a simulation study. Increasing the effective distances of a lure (i.e., lure attraction) and camera density both resulted in biased abundance estimates. Each estimator rarely recovered truth or suffered from convergence issues. Our results indicate that, when limited to unmarked estimators and the use of lures, camera traps alone are unlikely to produce abundance estimates with utility for brown treesnake management.

Estimating preharvest density, adult sex ratio, and fecundity of white-tailed deer using noninvasive sampling techniques

Adult sex ratio and fecundity (juveniles per female) are key population parameters in sustainable wildlife management, but inferring these requires abundance estimates of at least three age/sex classes of the population (male and female adults and juveniles). Prior to harvest, we used an array of 36 wildlife camera traps during 2 and 3 weeks in the early autumn of 2016 and 2017, respectively. We recorded white-tailed deer adult males, adult females, and fawns from the pictures. Simultaneously, we collected fecal DNA (fDNA) from 92 20 m × 20 m plots placed in 23 clusters of four plots between the camera traps. We identified individuals from fDNA samples with microsatellite markers and estimated the total sex ratio and population density using spatial capture-recapture (SCR). The fDNA-SCR analysis concluded equal sex ratio in the first year and female bias in the second year, and no difference in space use between sexes (fawns and adults combined). Camera information was analyzed in a spatial capture (SC) framework assuming an informative prior for animals' space use, either (a) as estimated by fDNA-SCR (same for all age/sex classes), (b) as assumed from the literature (space use of adult males larger than adult females and fawns), or (c) by inferring adult male space use from individually identified males from the camera pictures. These various SC approaches produced plausible inferences on fecundity, but also inferred total density to be lower than the estimate provided by fDNA-SCR in one of the study years. SC approaches where adult male and female were allowed to differ in their space use suggested the population had a female-biased adult sex ratio. In conclusion, SC approaches allowed estimating the preharvest population parameters of interest and provided conservative density estimates.

Roadside Car Surveys: Methodological Constraints and Solutions for Estimating Parrot Abundances across the World

Parrots stand out among birds because of their poor conservation status and the lack of available information on their population sizes and trends. Estimating parrot abundance is complicated by the high mobility, gregariousness, patchy distributions, and rarity of many species. Roadside car surveys can be useful to cover large areas and increase the probability of detecting spatially aggregated species or those occurring at very low densities. However, such surveys may be biased due to their inability to handle differences in detectability among species and habitats. We conducted 98 roadside surveys, covering > 57,000 km across 20 countries and the main world biomes, recording ca. 120,000 parrots from 137 species. We found that larger and more gregarious species are more easily visually detected and at greater distances, with variations among biomes. However, raw estimates of relative parrot abundances (individuals/km) were strongly correlated (r = 0.86–0.93) with parrot densities (individuals/km2) estimated through distance sampling (DS) models, showing that variability in abundances among species (>40 orders of magnitude) overcomes any potential detectability bias. While both methods provide similar results, DS cannot be used to study parrot communities or monitor the population trends of all parrot species as it requires a minimum of encounters that are not reached for most species (64% in our case), mainly the rarest and more threatened. However, DS may be the most suitable choice for some species-specific studies of common species. We summarize the strengths and weaknesses of both methods to guide researchers in choosing the best–fitting option for their particular research hypotheses, characteristics of the species studied, and logistical constraints.

Modelos de ocupación para datos de cámaras trampa

El uso de las cámaras trampa (CT) en la investigación de fauna silvestre puede generar conclusiones sesgadas cuando la detectabilidad imperfecta de especies no es considerada. Herramientas analíticas como los modelos de ocupación permiten estimar simultáneamente parámetros ecológicos corregidos por la probabilidad de detección. Sin embargo, es necesario implementar e interpretar de manera correcta los parámetros estimados por estos modelos para obtener inferencias con sentido biológico. Este trabajo presenta un marco conceptual base para diseñar de manera apropiada un análisis de ocupación por medio de datos de CT. Se discuten y se señalan recomendaciones generales para la definición de los elementos del modelo, el diseño del muestreo, así como estrategias de modelamiento estadísticos apropiadas dependiendo de los objetivos del estudio, las características de la especie y el tipo de datos obtenidos. Las decisiones tomadas por el investigador para definir cada uno de los componentes del modelo deben considerar la escala adecuada para que el fenómeno de estudio tenga sentido biológico. De esta manera, es posible generar inferencias y conclusiones robustas a partir de información de CT, lo que permite avanzar en el entendimiento de los mecanismos que subyacen a la ecología espacial de fauna silvestre y por lo tanto en su conservación.

Analysis of heterogeneity of inflation expectation based on genetic algorithm and time series model

The heterogeneity of inflation expectations, especially the residents’ inflation expectations, has a great influence on controlling the actual inflation rate and the effective implementation of my country’s monetary policy. In the process of monetary policy formulation, the monetary authorities need to pay more attention to the heterogeneous expectations among microeconomic individuals. This paper introduces the genetic algorithm, a new artificial intelligence method, to analyze the demand for the heterogeneity of inflation expectations and explains the basic steps to use it and how to apply it to explain problems in economics. Moreover, this paper uses a genetic algorithm-based generation overlap model to simulate the dynamic evolution of inflation heterogeneity among residents and the equilibrium selection process of price levels in a wide search space. The results of the simulation experiment show that it is of practical significance to use genetic algorithms to simulate the dynamic process of the heterogeneity of residents’ inflation expectations.

Analysis of the impact of inflation expectations based on machine learning intelligent models

Our country’s economic growth is overly dependent on government investment, and bank credit and money supply lack a strict monitoring mechanism. Therefore, rapid economic growth is always accompanied by inflation risks. In order to study the effect of inflation impact analysis, based on machine learning algorithms, this paper combines artificial intelligence technology to analyze the impact of inflation expectations, and constructs the central bank information disclosure index and inflation expectations index. Moreover, this paper will perform ADF unit root test on the data. In addition, after confirming that the data is stable, this paper uses the Markov Regime Transfer Vector Autoregressive (MSVAR) model and state-dependent impulse response function to test and analyze the effect of China’s central bank communication in guiding the formation of inflation expectations. Through research, we can see that the machine learning algorithm constructed in this paper has significant effects, which can provide a reference for the analysis of the impact of inflation expectations.

Reliability of animal counts and implications for the interpretation of trends

Population time series analysis is an integral part of conservation biology in the current context of global changes. To quantify changes in population size, wildlife counts only provide estimates because of various sources of error. When unaccounted for, such errors can obscure important ecological patterns and reduce confidence in the derived trend. In the case of highly gregarious species, which are common in the animal kingdom, the estimation of group size is an important potential bias, which is characterized by high variance among observers. In this context, it is crucial to quantify the impact of observer changes, inherent to population monitoring, on i) the minimum length of population time series required to detect significant trends and ii) the accuracy (bias and precision) of the trend estimate.We acquired group size estimation error data by an experimental protocol where 24 experienced observers conducted counting simulation tests on group sizes. We used this empirical data to simulate observations over 25 years of a declining population distributed over 100 sites. Five scenarios of changes in observer identity over time and sites were tested for each of three simulated trends (true population size evolving according to deterministic models parameterized with declines of 1.1%, 3.9% or 7.4% per year that justify respectively a "declining," "vulnerable" or "endangered" population under IUCN criteria).We found that under realistic field conditions observers detected the accurate value of the population trend in only 1.3% of the cases. Our results also show that trend estimates are similar if many observers are spatially distributed among the different sites, or if one single observer counts all sites. However, successive changes in observer identity over time lead to a clear decrease in the ability to reliably estimate a given population trend, and an increase in the number of years of monitoring required to adequately detect the trend.Minimizing temporal changes of observers improve the quality of count data and help taking appropriate management decisions and setting conservation priorities. The same occurs when increasing the number of observers spread over 100 sites. If the population surveyed is composed of few sites, then it is preferable to perform the survey by one observer. In this context, it is important to reconsider how we use estimated population trend values and potentially to scale our decisions according to the direction and duration of estimated trends, instead of setting too precise threshold values before action.

Community-level modelling of boreal forest mammal distribution in an oil sands landscape

Anthropogenic landscape disturbances are known to alter, destroy, and fragment habitat, which typically leads to biodiversity loss. The effects of landscape disturbance generally vary among species and depend on the nature of the disturbances, which may interact and result in synergistic effects. Western Canada's oil sands region experiences disturbances from forestry and energy sector activities as well as municipal and transportation infrastructure. The effects of those disturbances on single species have been studied and have been implicated in declines of the boreal woodland caribou (Rangifer tarandus caribou). Yet, the specific responses of the mammal community, and of functional groups such as prey and predators, to those interacting disturbances are still poorly known. We investigated the responses of black bear, grey wolf, coyote, fisher, lynx, red fox, American red squirrel, white-tailed deer, moose, caribou, and snowshoe hare to both natural habitat and disturbance associated with anthropogenic features within Alberta's northeast boreal forest. We used a novel community-level modelling framework on three years of camera-trap data collected in an oil sands landscape. This framework allowed us to identify the natural and anthropogenic features which explained the most variation in occurrence frequency among functional groups, as well as compare responses to linear and non-linear anthropogenic disturbance. Occurrence frequency by predators was better explained by anthropogenic features than by natural habitat. Both linear and non-linear anthropogenic features helped explain occurrence frequency by prey and predators, although the effects differed in magnitude and spatial scale. To better conserve boreal biodiversity, management actions should extend beyond a focus on caribou and wolves and aim to restore habitat across a diversity of anthropogenic disturbances and monitor the dynamics of the entire mammal community.

Abundance estimation of unmarked animals based on camera-trap data

The rapid improvement of camera traps in recent decades has revolutionized biodiversity monitoring. Despite clear applications in conservation science, camera traps have seldom been used to model the abundance of unmarked animal populations. We sought to summarize the challenges facing abundance estimation of unmarked animals, compile an overview of existing analytical frameworks, and provide guidance for practitioners seeking a suitable method. When a camera records multiple detections of an unmarked animal, one cannot determine whether the images represent multiple mobile individuals or a single individual repeatedly entering the camera viewshed. Furthermore, animal movement obfuscates a clear definition of the sampling area and, as a result, the area to which an abundance estimate corresponds. Recognizing these challenges, we identified 6 analytical approaches and reviewed 927 camera-trap studies published from 2014 to 2019 to assess the use and prevalence of each method. Only about 5% of the studies used any of the abundance-estimation methods we identified. Most of these studies estimated local abundance or covariate relationships rather than predicting abundance or density over broader areas. Next, for each analytical approach, we compiled the data requirements, assumptions, advantages, and disadvantages to help practitioners navigate the landscape of abundance estimation methods. When seeking an appropriate method, practitioners should evaluate the life history of the focal taxa, carefully define the area of the sampling frame, and consider what types of data collection are possible. The challenge of estimating abundance of unmarked animal populations persists; although multiple methods exist, no one method is optimal for camera-trap data under all circumstances. As analytical frameworks continue to evolve and abundance estimation of unmarked animals becomes increasingly common, camera traps will become even more important for informing conservation decision-making.

Line-transect versus point-transect sampling: the effects of survey area and survey effort on method efficiency for Geoffroy’s spider monkeys

Abstract ContextLine-transect sampling is considered to be a more efficient survey method than point-transect sampling to estimate population densities and abundance of many animal species. AimsIn the present study, we tested whether this claim holds true when surveying arboreal fast-moving primate species occurring at low densities, and whether the potential difference in efficiency can be explained by the difference in the size of the survey area between the methods. We further examined the impact of survey effort for point-transect sampling. MethodsWe conducted line- and point-transect sampling for Geoffroy’s spider monkeys (Ateles geoffroyi) in the same locations and compared the numbers of detected individual monkeys and the probability of their occurrence per survey between the two methods. We further compared the data from point-transect sampling gathered within three different waiting periods. Key resultsWe found a higher probability to detect monkeys and a higher number of monkeys during line-transect sampling than during point-transect sampling, but more spider monkeys were detected at point transects when controlling for the size of the survey area. More monkey detections were made during the first 10 min than during the second and third 10-min periods of point-transect surveys. ConclusionsWe showed that line-transect sampling is more efficient than point-transect sampling when surveying Geoffroy’s spider monkeys in a flat landscape of tropical forest with homogenous visibility. We discuss factors influencing survey results and recommend 20 min as the maximum waiting time at point transects when surveying arboreal mammals. ImplicationsOur study has provided a quantitative approach to compare efficiency across survey methods for fast-moving arboreal animals that occur at low densities, and supports the use of point-transect sampling in sites where line-transect sampling is not feasible, such as in human-modified landscapes.

New technologies in the mix: Assessing N-mixture models for abundance estimation using automated detection data from drone surveys

Reliable estimates of abundance are critical in effectively managing threatened species, but the feasibility of integrating data from wildlife surveys completed using advanced technologies such as remotely piloted aircraft systems (RPAS) and machine learning into abundance estimation methods such as N-mixture modeling is largely unknown due to the unique sources of detection errors associated with these technologies.We evaluated two modeling approaches for estimating the abundance of koalas detected automatically in RPAS imagery: (a) a generalized N-mixture model and (b) a modified Horvitz-Thompson (H-T) estimator method combining generalized linear models and generalized additive models for overall probability of detection, false detection, and duplicate detection. The final estimates from each model were compared to the true number of koalas present as determined by telemetry-assisted ground surveys.The modified H-T estimator approach performed best, with the true count of koalas captured within the 95% confidence intervals around the abundance estimates in all 4 surveys in the testing dataset (n = 138 detected objects), a particularly strong result given the difficulty in attaining accuracy found with previous methods.The results suggested that N-mixture models in their current form may not be the most appropriate approach to estimating the abundance of wildlife detected in RPAS surveys with automated detection, and accurate estimates could be made with approaches that account for spurious detections.

Optimization of capture-recapture monitoring of elusive species illustrated with a threatened grasshopper

Information on population sizes and trends of threatened species is essential for their conservation, but obtaining reliable estimates can be challenging. We devised a method to improve the precision of estimates of population size obtained from capture-recapture studies for species with low capture and recapture probabilities and short seasonal activity, illustrated with population data of an elusive grasshopper (Prionotropis rhodanica). We used data from 5 capture-recapture studies to identify methodological and environmental factors affecting capture and recapture probabilities and estimates of population size. In a simulation, we used the population size and capture and recapture probability estimates obtained from the field studies to identify the minimum number of sampling occasions needed to obtain unbiased and robust estimates of population size. Based on these results we optimized the capture-recapture design, implemented it in 2 additional studies, and compared their precision with those of the nonoptimized studies. Additionally, we simulated scenarios based on thresholds of population size in criteria C and D of the International Union for Conservation of Nature (IUCN) Red List to investigate whether estimates of population size for elusive species can reliably inform red-list assessments. Identifying parameters that affect capture and recapture probabilities (for the grasshopper time since emergence of first adults) and optimizing field protocols based on this information reduced study effort (-6% to -27% sampling occasions) and provided more precise estimates of population size (reduced coefficient of variation) compared with nonoptimized studies. Estimates of population size from the scenarios based on the IUCN thresholds were mostly unbiased and robust (only the combination of very small populations and little study effort produced unreliable estimates), suggesting capture-recapture can be considered reliable for informing red-list assessments. Although capture-recapture remains difficult and costly for elusive species, our optimization procedure can help determine efficient protocols to increase data quality and minimize monitoring effort.