Population Count Data Research Articles

Small-area population forecasting in a segregated city using density-functional fluctuation theory

Policy decisions concerning housing, transportation, and resource allocation would all benefit from accurate small-area population forecasts. However, despite the success of regional-scale migration models, developing neighborhood-scale forecasts remains a challenge due to the complex nature of residential choice. Here, we introduce an innovative approach to this challenge by extending density-functional fluctuation theory (DFFT), a proven approach for modeling group spatial behavior in biological systems, to predict small-area population shifts over time. The DFFT method uses observed fluctuations in small-area populations to disentangle and extract effective social and spatial drivers of segregation, and then uses this information to forecast intra-regional migration. To demonstrate the efficacy of our approach in a controlled setting, we consider a simulated city constructed from a Schelling-type model. Our findings indicate that even without direct access to the underlying agent preferences, DFFT accurately predicts how broader demographic changes at the city scale percolate to small-area populations. In particular, our results demonstrate the ability of DFFT to incorporate the impacts of segregation into small-area population forecasting using interactions inferred solely from steady-state population count data.

A unified approach to long-term population monitoring of grizzly bears in the Greater Yellowstone Ecosystem

Long-term wildlife research and monitoring programs strive to maintain consistent data collections and analytical methods. Incorporating new techniques is important but can render data sets incongruent and limit their potential to discern trends in demographic parameters. Integrated population models (IPMs) can address these limitations by combining data sources that may span different periods into a unified statistical framework while providing a holistic view of population dynamics. We developed an IPM in a Bayesian framework for grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem. We coupled demographic data with multiple, independent population count data to link annual changes in abundance with vital rates over 4 decades (1983–2023). Abundance increased threefold from an estimated 270 individuals in 1984 to 1030 individuals in 2023. Parameter estimates indicated survival of bears ≥2 years of age was high, contributing to robust population growth during the 1980s (λ = 1.023 [50 % interquartile range = 0.993–1.082]) and 1990s (λ = 1.064 [1.023–1.103]). A slowing of population growth started around 2000 (2000s: λ = 1.030 [0.989–1.068]) and continued into the 2010s (λ = 1.021 [0.985–1.057]), due primarily to reductions in survival of bears <2 years of age. These findings corroborate previous research that identified density-dependent effects as a likely cause. The IPM framework provided greater certainty and understanding regarding the dynamic demographic characteristics of the population and serves as a powerful monitoring tool for this long-lived species. Implementation of the IPM allows timely dissemination of demographic data to help inform adaptive management strategies and policy decisions necessary for the continued management and conservation of this population. This robust and flexible monitoring system allows scientists to investigate the effects of a changing ecosystem on population dynamics, incorporate new data sources and statistical models, and respond to changes in monitoring needs for the population. We highlight the efficacy of the IPM in estimating and tracking demographic parameters for a long-lived species, while accommodating shifts in monitoring techniques and data collections typical of long-term wildlife conservation programs worldwide.

Intimate Partner Violence Among Service Members and Veterans: Differences by Sex and Rurality.

Among military service members and veterans (SMVs), factors unique to military service may contribute to an elevated risk of experiencing intimate partner violence (IPV) victimization. Although rurality has been established as a risk factor for IPV, differences in IPV victimization by rural- urban dwelling location, SMV status, and sex have not been explored. The purpose of this study was to estimate the rate of IPV victimization in rural and urban areas in the United States by SMV status and sex. We obtained Behavioral Risk Factor Surveillance System data (BRFSS; n = 18,755); fit a mixed-effects, multilevel generalized linear model to the data for IPV victimization; and linked the model to U.S. Census Bureau population count data. We generated predicted estimates of IPV for SMVs and civilians separately by sex in rural and urban areas. The direct IPV victimization prevalence rate for the entire BRFSS sample was 16.90%. Substantial variation in model-based IPV prevalence was observed across subgroups. Female SMVs (rural = 23.54%, 95% confidence interval [CI] [17.33, 30.02]; urban = 23.34%, 95% CI [17.48, 30.17]) had higher IPV victimization rates than female civilians (rural = 14.55%, 95% CI [13.06, 16.37]; urban = 14.50%, 95% CI [13.19, 16.34]), whereas male civilians (rural = 8.06%, 95% CI [7.19, 9.08]; urban = 8.02%, 95% CI [7.27, 9.02]) had higher IPV victimization rates than male SMVs (rural = 7.21%, 95% CI [6.03, 8.47]; urban = 7.17%, 95% CI [6.00, 8.41]). Programming for preventing and assisting in recovering from IPV exposure should target rural-dwelling female SMVs.

Population demography of the Blackbuck Antilope cervicapra (Cetartiodactyla: Bovidae) at Point Calimere Wildlife Sanctuary, India

Demographic research utilizing vital rates and life tables is a standard aspect of planning protection and management strategies for wildlife populations. The Blackbuck Antilope cervicapra population at Point Calimere Wildlife Sanctuary, Tamil Nadu, has undergone fluctuations in recent decades. The cause remains elusive, and conservation efforts may be hampered by a lack of population data. This study aimed to estimate demographic parameters using population count and age-sex classification data collected for the years 2017─2020. The overall mean population estimate derived from line-transect distance sampling was 719, with annual estimates of 716, 727, 711, and 722 for the years 2017–2020 respectively. In total, 64% of Blackbucks counted were adults, 19% subadults, and 17% fawns. Mortality was highest for adult and subadult classes for the composite female class, and fawn mortality was 20%. The net reproductive rate (R0) was as low as 3.28 offspring per generation contrasted with a rather longer mean generation time (G) as 4.75 years. Thus, the study observes a decrease in Blackbuck numbers postulated in earlier research to be driven principally by a conglomerate of factors, including reduction of usable space and interspecific resource competition. Our findings provide a baseline demography of the species and highlights the value of long-term demographic monitoring of age sex classes to understand the evolution of life histories.

English

Thrips, Thrips tabaci (Thysanoptera: Thripidae) on the cotton plant is an extremely invasive and destructive pest that reduces yield. Field studies were conducted at Werer Agricultural Research Center, Middle Awash, Afar, Ethiopia, for two consecutive years (2017 and 2018) to determine the effectiveness of different synthetic chemical insecticides for the control of cotton thrips (T. tabaci) on the Deltapine-90 cotton variety. A total of eleven treatments with untreated control were laid out in a Randomized Complete Block Design (RCBD) replicated thrice per treatment. The field was sprayed two times with an economic threshold level attained in two weeks intervals after the first round of data. Thrips population count data on thrips populations were collected before and after three, five, seven and ten days of insecticide application. The yield and yield component, and economic returns data were also collected. The percent efficacy was calculated using a modified Abbott&#39;s formula. The efficacy of the insecticide Rectro 20% Sc, Imidacloprid 20% SC, Closer 240% SC and Dimethoate 40% EC resulted in fewer numbers of thrips and higher yields than Diazinon 60% EC, Chlorpyrifos 48% EC, Chloropyrafose 36 SC, Deltamethrin 06% and Curadore 45%. The economic analysis of the insecticides chemical showed good cost-benefit for Rectro 20% SC (2.55), Closer 240% SC (1.93) and Dimethoate 40% EC (1.49) with good advantage. Additionally, the protection of cotton thrips using the insecticide chemicals namely Imdacloropride 20% SC, Rectro 20% Sc and Curador 45% EC would return 1.33, 1.24 and 1.24 birr return. The result confirmed that the application of more effective insecticides when the thrips population was high resulted in better control and higher economic returns. Therefore, the use of sequential applications in the insecticides, Closer 240% SC, Dimethoate 40% EC, Rectro 20%SC and Imidacloprid is recommended to manage the cotton thrips in the middle awash areas of Ethiopia. Key words: Insect pest, resistance, management, insecticides, sequential, economic returns.

Near real time monitoring and forecasting for COVID-19 situational awareness

In the opening months of the pandemic, the need for situational awareness was urgent. Forecasting models such as the Susceptible-Infectious-Recovered (SIR) model were hampered by limited testing data and key information on mobility, contact tracing, and local policy variations would not be consistently available for months. New case counts from sources like John Hopkins University and the NY Times were systematically reliable. Using these data, we developed the novel COVID County Situational Awareness Tool (CCSAT) for reliable monitoring and decision support. In CCSAT, we developed a retrospective seven-day moving window semantic map of county-level disease magnitude and acceleration that smoothed noisy daily variations. We also developed a novel Bayesian model that reliably forecasted county-level magnitude and acceleration for the upcoming week based on population and new case count data. Together these formed a robust operational update including county-level maps of new case rate changes, estimates of new cases in the upcoming week, and measures of model reliability. We found CCSAT provided stable, reliable estimates across the seven-day time window, with the greatest errors occurring in cases of anomalous, single day spikes. In this paper, we provide CCSAT details and apply it to a single week in June 2020.

Health risk assessment from exposure to dissolved trace element concentration in drinking groundwater resources of Central Ganga Alluvial Plain: a case study of Lucknow region

ABSTRACT This study explores ‘Indicator Kriging’ approach for assessment of health risk from exposure to trace elements concentration in drinking water resources of the Central Ganga Alluvial Plain (CGAP), northern India. The estimates for were generated using analysis of groundwater samples (n = 100) collected from the Lucknow monitoring area to map the predicted area of health risk. The predicted probability maps have reclassified into a unified scale to generate Trace Element Risk Index (TERI), which has further integrated with human population count data to generate Health Risk Index of Lucknow. The results indicate that the risk is potentially alarming in urban areas as relatively high there are referring to the local (point) sources of contamination. Approximately 23.15% human population residing in about 69.77% of the total area is at moderate-to-high health risk probability. The findings of this study could help planning substantial remediation measures on long-term basis.

A machine learning analysis of drought and rural population change on the North American Great Plains since the 1970s.

Machine learning techniques have to date not been widely used in population-environment research, but represent a promising tool for identifying relationships between environmental variables and population outcomes. They may be particularly useful for instances where the nature of the relationship is not obvious or not easily detected using other methods, or where the relationship potentially varies across spatial scales within a given study unit. Machine learning techniques may also help the researcher identify the relative strength of influence of specific variables within a larger set of interacting ones, and so provide a useful methodological approach for exploratory research. In this study, we use machine learning techniques in the form of random forest and regression tree analyses to look for possible connections between drought and rural population loss on the North American Great Plains between 1970 and 2020. In doing so, we analyzed four decades of population count data (at county-size spatial scales), monthly climate data, and Palmer Drought Severity Index scores for Canada and the USA at multiple spatial scales (regional, sub-regional, national, and county/census division levels), along with county level irrigation data. We found that in some parts of Saskatchewan and the Dakotas − particularly those areas that fall within more temperate/less arid ecological sub-regions − drought conditions in the middle years of the 1970s had a significant association with rural population losses. A similar but weaker association was identified in a small cluster of North Dakota counties in the 1990s. Our models detected few links between drought and rural population loss in other decades or in other parts of the Great Plains. Based on R-squared results, models for US portions of the Plains generally exhibited stronger drought-population loss associations than did Canadian portions, and temperate ecological sub-regions exhibited stronger associations than did more arid sub-regions. Irrigation rates showed no significant influence on population loss. This article focuses on describing the methodological steps, considerations, and benefits of employing this type of machine learning approach to investigating connections between drought and rural population change.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11111-022-00399-9.

412. Estimating the Impact of School Classroom Sizes on the Probability of Severe Acute Respiratory Syndrome Coronavirus-2 Infectivity or Exposure

BackgroundDespite schools reopening across the United States in communities with low and high Coronavirus disease 2019 (COVID-19) prevalence, data remain scarce about the effect of classroom size on the transmission of severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) within schools. This study estimates the effect of classroom size on the risk of COVID-19 infection in a closed classroom cohort for varying age groups locally in Durham, North Carolina. Total number of Coronavirus Disease 2019 (COVID-19) infections over a 28-day follow-up period for varying classroom reproduction number (R0) and varying classroom cohort sizes of 15 students, 30 students and 100 students in Durham County, North Carolina. MethodsUsing publicly available population and COVID-19 case count data from Durham County, we calculated a weekly average number of new confirmed COVID-19 cases per week between May 3, 2020 and August 22, 2020 according to age categories: < 5 years, 5-9 years, 10-14 years, and 15-19 years. We collated average classroom cohort sizes and enrollment data for each age group by grade level of education for the first month of the 2019-2020 academic school year. Then, using a SEIR compartmental model, we calculated the number of susceptible (S), exposed (E), infectious (I) and recovered (R) students in a cohort size of 15, 30 and 100 students, modelling for classroom reproduction number (R0) of 0.5, 1.5 and 2.5 within a closed classroom cohort over a 14-day and 28-day follow-up period using age group-specific COVID-19 prevalence rates.ResultsThe SEIR model estimated that the increase in cohort size resulted in up to 5 new COVID-19 infections per 10,000 students whereas the classroom R0 had a stronger effect, with up to 88 new infections per 10,000 students in a closed classroom cohort over time. When comparing different follow-up periods in a closed cohort with R0 of 0.5, we estimated 12 more infected students per 10,000 students over 28 days as compared to 14 days irrespective of cohort size. With a R0 of 2.5, there were 49 more infected students per 10,000 students over 28 days as compared to 14 days.ConclusionClassroom R0 had a stronger impact in reducing school-based COVID-19 transmission events as compared to cohort size. Additionally, earlier isolation of newly infected students in a closed cohort resulted in fewer new COVID-19 infections within that group. Mitigation strategies should target promoting safe practices within the school setting including early quarantine of newly identified contacts and minimizing COVID-19 community prevalence.Disclosures Michael J. Smith, MD, M.S.C.E, Merck (Grant/Research Support)Pfizer (Grant/Research Support)

Multilevel small area estimation for county-level prevalence of colorectal cancer screening test use in the United States using 2018 data

Purpose—National screening estimates mask county-level variations. We aimed to generate county-level colorectal cancer (CRC) screening prevalence estimates for 2018 among adults aged 50–75 years and identify counties with low screening prevalence. Methods—We combined individual-level county data from the 2018 Behavioral Risk Factor Surveillance System (BRFSS) (n = 204,947) with the 2018 American Community Survey county poverty data as a covariate, and the 2018 U.S. Census county population count data to generate county-level prevalence estimates for being current with any CRC screening test, colonoscopy, and home stool blood test. Because BRFSS is a state-based survey, and because some counties did not have samples for analysis, we used correlation coefficients to test internal consistency between model-based and BRFSS state estimates. Results—Correlation coefficients tests were ≥0.97. Model-based national prevalence for any test was 69.9% (95% CI, 69.5% –70.4%) suggesting 30% are not current with screening test use. State mean estimates ranged from 62.1% in Alaska and Wyoming to 76.6% in Maine and Massachusetts. County mean estimates ranged from 42.2% in Alaska to 80.0% in Florida and Rhode Island. Most tests were performed with colonoscopy. Conclusions—Estimates across all U.S. counties showed large variations. Estimates may be informative for planning by states and local screening programs.

Automatic Building Detection with Polygonizing and Attribute Extraction from High-Resolution Images

Buildings can be introduced as a fundamental element for forming a city. Therefore, up-to-date building maps have become vital for many applications, including urban mapping and urban expansion analysis. With the development of deep learning, segmenting building footprints from high-resolution remote sensing imagery has become a subject of intense study. Here, a modified version of the U-Net architecture with a combination of pre- and post-processing techniques was developed to extract building footprints from high-resolution aerial imagery and unmanned aerial vehicle (UAV) imagery. Data pre-processing with the logarithmic correction image enhancing algorithm showed the most significant improvement in the building detection accuracy for aerial images; meanwhile, the CLAHE algorithm improved the most concerning UAV images. This study developed a post-processing technique using polygonizing and polygon smoothing called the Douglas–Peucker algorithm, which made the building output directly ready to use for different applications. The attribute information, land use data, and population count data were applied using two open datasets. In addition, the building area and perimeter of each building were calculated as geometric attributes.

Large conservation opportunities exist in >90% of tropic-subtropic coastal habitats adjacent to cities

Large conservation opportunities exist in >90% of tropic-subtropic coastal habitats adjacent to cities

Fine-scale estimation of key life-history parameters of malaria vectors: implications for next-generation vector control technologies

BackgroundMosquito control has the potential to significantly reduce malaria burden on a region, but to influence public health policy must also show cost-effectiveness. Gaps in our knowledge of mosquito population dynamics mean that mathematical modelling of vector control interventions have typically made simplifying assumptions about key aspects of mosquito ecology. Often, these assumptions can distort the predicted efficacy of vector control, particularly next-generation tools such as gene drive, which are highly sensitive to local mosquito dynamics.MethodsWe developed a discrete-time stochastic mathematical model of mosquito population dynamics to explore the fine-scale behaviour of egg-laying and larval density dependence on parameter estimation. The model was fitted to longitudinal mosquito population count data using particle Markov chain Monte Carlo methods.ResultsBy modelling fine-scale behaviour of egg-laying under varying density dependence scenarios we refine our life history parameter estimates, and in particular we see how model assumptions affect population growth rate (Rm), a crucial determinate of vector control efficacy.ConclusionsSubsequent application of these new parameter estimates to gene drive models show how the understanding and implementation of fine-scale processes, when deriving parameter estimates, may have a profound influence on successful vector control. The consequences of this may be of crucial interest when devising future public health policy.Graphical abstract

Fitting stochastic predator–prey models using both population density and kill rate data

Most mechanistic predator–prey modelling has involved either parameterization from process rate data or inverse modelling. Here, we take a median road: we aim at identifying the potential benefits of combining datasets, when both population growth and predation processes are viewed as stochastic. We fit a discrete-time, stochastic predator–prey model of the Leslie type to simulated time series of densities and kill rate data. Our model has both environmental stochasticity in the growth rates and interaction stochasticity, i.e., a stochastic functional response. We examine what the kill rate data brings to the quality of the estimates, and whether estimation is possible (for various time series lengths) solely with time series of population counts or biomass data. Both Bayesian and frequentist estimation are performed, providing multiple ways to check model identifiability. The Fisher Information Matrix suggests that models with and without kill rate data are all identifiable, although correlations remain between parameters that belong to the same functional form. However, our results show that if the attractor is a fixed point in the absence of stochasticity, identifying parameters in practice requires kill rate data as a complement to the time series of population densities, due to the relatively flat likelihood. Only noisy limit cycle attractors can be identified directly from population count data (as in inverse modelling), although even in this case, adding kill rate data – including in small amounts – can make the estimates much more precise. Overall, we show that under process stochasticity in interaction rates, interaction data might be essential to obtain identifiable dynamical models for multiple species. These results may extend to other biotic interactions than predation, for which similar models combining interaction rates and population counts could be developed.

Is Habitat Constraining Bighorn Sheep Restoration? A Case Study

ABSTRACTRocky Mountain bighorn sheep (Ovis canadensis) restoration continues to be a challenge throughout western North America despite nearly a century of efforts dedicated to the species' recovery. Though bighorn sheep restoration may be constrained by several environmental factors and behavioral tendencies, areas with unrealized restoration potential may exist if novel restoration strategies are considered. We used global positioning system (GPS) location data from 27 female bighorn sheep within the southern portion of the Madison Range in southwest Montana, USA, 2015–2017, to develop and validate winter and summer habitat models, which we extrapolated throughout the entire Madison Range to identify potential seasonal habitat. We estimated potential bighorn sheep minimum population estimates within the extrapolation area by linking our top‐ranked winter habitat model to population count data. During summer, female bighorn sheep selected areas characterized by rugged and steep terrain, reduced canopy cover, southwestern aspects, and ridgelines. During winter, female bighorn sheep selected areas characterized by low elevations, southwestern aspects, steep slopes, reduced canopy cover, ridgelines, high normalized difference vegetation index amplitude, and areas close to steep terrain. Predicted summer habitat was concentrated along the high‐elevation ridgelines associated with steep slopes and reduced canopy cover. Predicted winter habitat occurred in a non‐contiguous distribution primarily along the low‐elevation, southwest‐facing aspects along the western slopes of the Madison Range. Our results suggest that the Madison Range may be capable of supporting 780–1,730 animals, which is 2–4 times the number of bighorn sheep currently observed within the range. Further, our findings provide managers with a quantification of female bighorn sheep habitat and suggests that a strategy focused on establishing a metapopulation through a series of within‐range translocations may enhance bighorn sheep restoration. We suggest that similar restoration opportunities may be common in other unoccupied areas of bighorn sheep historical range. © 2020 The Wildlife Society.

An improved understanding of ungulate population dynamics using count data: Insights from western Montana.

Understanding the dynamics of ungulate populations is critical given their ecological and economic importance. In particular, the ability to evaluate the evidence for potential drivers of variation in population trajectories is important for informed management. However, the use of age ratio data (e.g., juveniles:adult females) as an index of variation in population dynamics is hindered by a lack of statistical power and difficult interpretation. Here, we show that the use of a population model based on count, classification and harvest data can dramatically improve the understanding of ungulate population dynamics by: 1) providing estimates of vital rates (e.g., per capita recruitment and population growth) that are easier to interpret and more useful to managers than age ratios and 2) increasing the power to assess potential sources of variation in key vital rates. We used a time series of elk (Cervus canadensis) spring count and classification data (2004 to 2016) and fall harvest data from hunting districts in western Montana to construct a population model to estimate vital rates and assess evidence for an association between a series of environmental covariates and indices of predator abundance on per capita recruitment rates of elk calves. Our results suggest that per capita recruitment rates were negatively associated with cold and wet springs, and severe winters, and positively associated with summer precipitation. In contrast, an analysis of the raw age ratio data failed to detect these relationships. Our approach based on a population model provided estimates of the region-wide mean per capita recruitment rate (mean = 0.25, 90% CI = 0.21, 0.29), temporal variation in hunting-district-specific recruitment rates (minimum = 0.09; 90% CI = [0.07, 0.11], maximum = 0.43; 90% CI = [0.38, 0.48]), and annual population growth rates (minimum = 0.83; 90% CI = [0.78, 0.87], maximum = 1.20; 90% CI = [1.11, 1.29]). We recommend using routinely collected population count and classification data and a population modeling approach rather than interpreting estimated age ratios as a substantial improvement in understanding population dynamics.

Least Tern (Sternula antillarum) Population Response to Water Levels on Cheyenne River and Oahe Reservoir, South Dakota, USA

Consideration of annual population count data, stream flows, water levels, and nesting habitat availability over a 29-year period suggest that Least Terns (Sternula antillarum athalossos) nesting along the Cheyenne River and Oahe Reservoir in South Dakota, USA, select areas primarily in response to reservoir water levels early in the breeding season. Generalized linear models were selected in an information-theoretic framework and showed important relationships between adult Least Tern numbers and reservoir levels, as well as a negative long-term temporal trend in both survey areas. Reservoir levels alone could not account for the temporal trend. The number of adult Least Terns on the Cheyenne River was positively correlated (R = 0.668) with mean May reservoir water levels—a more important relationship than that between adult numbers and river levels. The number of adults on the Oahe Reservoir was negatively correlated (R = -0.573) with mean May reservoir water levels. Numbers of adults on the Cheyenne River and on Oahe Reservoir were negatively correlated (R = -0.684), suggesting that birds may interact as one population across the two areas. This information should help managers to consider ecological relationships among segments of the Least Tern population on the Missouri River and its tributaries and options for managing Least Terns on the Cheyenne River, Oahe Reservoir, and elsewhere on the Missouri River system.

Integrating data from multiple sources for insights into demographic processes: Simulation studies and proof of concept for hierarchical change-in-ratio models.

We developed a model for estimating demographic rates and population abundance based on multiple data sets revealing information about population age- and sex structure. Such models have previously been described in the literature as change-in-ratio models, but we extend the applicability of the models by i) using time series data allowing the full temporal dynamics to be modelled, by ii) casting the model in an explicit hierarchical modelling framework, and by iii) estimating parameters based on Bayesian inference. Based on sensitivity analyses we conclude that the approach developed here is able to obtain estimates of demographic rate with high precision whenever unbiased data of population structure are available. Our simulations revealed that this was true also when data on population abundance are not available or not included in the modelling framework. Nevertheless, when data on population structure are biased due to different observability of different age- and sex categories this will affect estimates of all demographic rates. Estimates of population size is particularly sensitive to such biases, whereas demographic rates can be relatively precisely estimated even with biased observation data as long as the bias is not severe. We then use the models to estimate demographic rates and population abundance for two Norwegian reindeer (Rangifer tarandus) populations where age-sex data were available for all harvested animals, and where population structure surveys were carried out in early summer (after calving) and late fall (after hunting season), and population size is counted in winter. We found that demographic rates were similar regardless whether we include population count data in the modelling, but that the estimated population size is affected by this decision. This suggest that monitoring programs that focus on population age- and sex structure will benefit from collecting additional data that allow estimation of observability for different age- and sex classes. In addition, our sensitivity analysis suggests that focusing monitoring towards changes in demographic rates might be more feasible than monitoring abundance in many situations where data on population age- and sex structure can be collected.

Effects of an Extreme Flood Event on Federally Endangered Diamond Darter Abundances

Extreme flood events can substantially affect riverine systems, modifying in-stream habitat and influencing fish assemblages and densities. Rare species are especially vulnerable to these disturbance events because of their small population size and often reduced phenotypic heterogeneity. In June 2016 the lower Elk River in West Virginia experienced severe flooding, resulting in a peak discharge that exceeded the 0.005 annual exceedance probability (>200 y flood) in the main stem. We obtained pre-flood and post-flood population count data and estimated abundances for one cohort of the federally endangered Diamond Darter (Crystallaria cincotta) at 15 sites. While both the total count data and total estimated abundance decreased following the flood, our analyses did not indicate the extreme flood event strongly impacted Diamond Darter abundance. This indicates individuals are able to withstand high velocities and resist displacement or mortality. In addition site-level abundances were estimated at three sentinel sites during 2015 and 2016 using a multinomial N-mixture model that accounted for variation in detectability resulting from water temperature. Mean estimated abundance varied among the three sites and between the 2 y. Our results suggest there is substantial variation in year-class strength between the two cohorts we sampled. It is suggested that survey efforts at established sentinel sites be continued on an annual basis in order to help determine factors influencing year-class strength.

Climate and density dependence cause changes in adult sex ratio in a large Arctic herbivore

AbstractVariation in adult sex ratio (ASR) affects population demography and dynamics of large mammals. The mechanisms behind this variation are largely unclear, but may be partly related to climatic drivers and density dependence operating differently on the adult male and female segments of the population. Here, we examine such drivers of annual changes in ASR in the predator‐free wild Svalbard reindeer (Rangifer tarandus platyrhynchus), a high Arctic subspecies whose population dynamics are shaped by climate. Using up to 35 year long time‐series of population count data from two populations, we disentangle drivers of fluctuations in ASR by first analyzing how climate and density dependence affect sex‐specific adult population growth rates through effects on mortality. There were a positive population size trend and an overall female bias in ASR throughout the study period. Increased winter precipitation, a proxy for the harshness of winter feeding conditions, was found to significantly reduce adult population growth rates through reduced survival in males, but not in females. However, increased population size tended to cause a stronger immediate decline in female than in male adult population growth rates. As a consequence, the female bias in ASR increased with harsher winter conditions and declined with higher population size. As expected from the increased frequency of rainy and icy winters due to climate warming, a recent trend toward increased female bias in ASR was evident. This demonstrates that climatic drivers of both short‐term fluctuations and long‐term trends in demography need to be accounted for in the management and population dynamic predictions of Arctic ungulates.