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Abundance indices play a crucial role in monitoring and assessing fish population dynamics. Fishery-independent surveys are commonly favored for deriving abundance indices because they follow standardized or randomized designs, ensuring spatiotemporal consistency in representative and unbiased sampling. However, modifications to the survey protocol may be necessary to accommodate changes in survey goals and logistic difficulty. When the survey undergoes changes, calibration is often needed to remove variability that is unrelated to changes in abundance. We evaluated a long-term monitoring program, the Long River Survey (LRS) in the Hudson River Estuary (HRE), to illustrate the process of calibrating survey data to account for the effects of changing sampling protocol. The LRS provided valuable ichthyoplankton data from 1974 to 2017, but inconsistencies in sampling timing, location, and gears resulted in challenges in interpreting and comparing the fish abundance data in the HRE. Generalized Additive Models were developed for five species at various life stages, aiming to mitigate the impact of sampling protocol changes. Model validation results suggest the consistent performance of the developed models with varying lengths of time series. This study indicates that changes in the sampling protocol can introduce biases in the estimates of abundance indices and that the model-based estimates can improve the reliability and accuracy of the survey abundance indices. The model-estimated sampling effects for each species and life stage provide critical information and valuable insights for designing future sampling protocols.

Although forest elephants (Loxodonta cyclotis) are known to use logged forests, the impact of selective logging on this critically endangered species has never been well established. Considering the potential of some logged areas to serve as other effective area‐based conservation measures, aligning with the Convention on Biological Diversity's 30/30 initiative, we aimed to assess the short‐term impacts of logging on three population parameters: the presence, abundance, and activity of forest elephants. Combining camera traps and dung surveys before and after logging operations, we assessed the response of forest elephants in a certified timber concession in Gabon. Encouragingly, we found no negative effects of logging on the three population parameters studied. There was no discernible change in the presence of elephants after logging, and their abundance actually increased (relative abundance index from 1.56 to 2.59; p‐value 0.0139) at one of the study sites. Activity patterns were also unaffected, showing sustained activity during daylight hours. We also identified the logging‐associated factors that have significantly influenced forest elephant's abundance. Notably, logging intensity, as measured by the average number of trees harvested per hectare, and road density were positively correlated with the number of forest elephant observations (logging intensity effect: 0.2992, p‐value 0.035; road density effect: 0.3628, p‐value 0.060). As global conservation goals evolve, this research provides important insights into the coexistence of well‐managed industrial activities and the conservation of endangered species, highlighting the need to include responsibly managed timber concessions in future conservation strategies. It also underscores the importance of wider adoption of sustainable practices such as low‐impact logging, promoted by certification schemes such as FSC or PEFC, to secure the future of central African forests and their unique wildlife. This study assesses the impact of selective logging on forest elephants (Loxodonta cyclotis) in Gabon. Using camera traps and dung surveys, we found no negative effects on their presence, abundance, or activity. The results suggest that responsibly managed timber concessions can support forest elephant conservation.

Background: Seasonal activity patterns of mosquitoes are essential as baseline knowledge to understand the transmis­sion dynamics of vector-borne diseases. This study was conducted to evaluate the monthly dynamics of the mosquito populations and their relation to meteorological factors in Mazandaran Province, north of Iran. Methods: Mosquito adults and larvae were collected from 16 counties of Mazandaran Province using different sam­pling techniques, once a month from May to December 2014. “Index of Species Abundance” (ISA) along with “Stand­ardized ISA” (SISA) was used for assessing the most abundant species of mosquitoes based on the explanations of Rob­ert and Hsi. Pearson’s correlation coefficient (R) was used to assess the relationships between the monthly population fluctuations and meteorological variables. Results: Overall, 23750 mosquitoes belonging to four genera and nineteen species were collected and identified. The highest population density of mosquitoes was in July and the lowest in May. The ISA/SISA indices for Culex pipiens were both 1 for larvae and 1.25/0.973 for adults in total catch performed in human dwellings. For Cx. tritaeniorhynchus, the ISA/SISA were 1.68/0.938 in pit shelter method. A significant positive correlation was observed between population fluctuations of Cx. tritaeniorhynchus and mean temperature (R: 0.766, P< 0.027). Conclusions: The results indicated that the mosquitoes are more active in July, and Cx. pipiens and Cx. tritaeniorhyn­chus were the most abundant species. Considering the potential of these species as vectors of numerous pathogens, con­trol programs can be planed based on their monthly activity pattern in the area.

Citizen scientists are increasingly engaged in gathering biodiversity information, but trade-offs are often required between public engagement goals and reliable data collection. We compared population estimates for 18 widespread butterfly species derived from the first 4 years (2011-2014) of a short-duration citizen science project (Big Butterfly Count [BBC]) with those from long-running, standardized monitoring data collected by experienced observers (U.K. Butterfly Monitoring Scheme (UKBMSJ). BBC data are gathered during an annual 3-week period, whereas UKBMS sampling takes place over 6 months each year. An initial comparison with UKBMS data restricted to the 3-week BBC period revealed that species population changes were significantly correlated between the 2 sources. The short-duration sampling season rendered BBC counts susceptible to bias caused by interannual phenological variation in the timing of species' flight periods. The BBC counts were positively related to butterfly phenology and sampling effort. Annual estimates of species abundance and population trends predicted from models including BBC data and weather covariates as a proxy for phenology correlated significantly with those derived from UKBMS data. Overall, citizen science data obtained using a simple sampling protocol produced comparable estimates of butterfly species abundance to data collected through standardized monitoring methods. Although caution is urged in extrapolating from this U.K. study of a small number of common, conspicuous insects, we found that mass-participation citizen science can simultaneously contribute to public engagement and biodiversity monitoring. Mass-participation citizen science is not an adequate replacementfor standardized biodiversity monitoring but may extend and complement it (e.g., through sampling different land-use types), as well as serving to reconnect an increasingly urban human population with nature. Los ciudadanos científicos cada vez participan más en la recopilación de información sobre la biodiversidad, pero comúnmente se requieren compensaciones entre los objetivos de participación pública y la recolección confiable de datos. Comparamos las estimaciones poblacionales para 18 especies de mariposas de extensión amplia derivados de los primeros cuatro años (2011 - 2014) de un proyecto de ciencia ciudadana de corta duración (Gran Conteo de Mariposas [GCM]) con aquellos estimados de datos de largo plazo y monitoreo estandarizado recolectados por observadores experimentados (Esquema de Monitoreo de Mariposas del Reino Unido [EMMRU]). Los datos del GCM son recopilados durante un periodo anual de tres semanas, mientras que los muestreos del EMMRU se realizan durante seis meses cada año. Una comparación inicial con los datos del EMMRU restringida al periodo de tres semanas del GCM reveló que los cambios en la población de las especies estuvieron correlacionados significativamente entre las dos fuentes. La corta duración de la temporada de muestreo dejó a los conteos del GCM susceptibles al sesgo causado por la variación fenológica interanual en la cadencia de los periodos de vuelo de las especies. Los conteos del GCM estuvieron relacionados positivamente con la fenología de las mariposas y el esfuerzo del muestro. Los estimados anuales de la abundancia de especies y las tendencias poblacionales pronosticadas a partir de modelos que incluían datos del GCM y covarianzas del clima como sustitutos de la fenología se correlacionaron significativamente con aquellos derivados de los datos del EMMRU. En general, los datos de la ciencia ciudadana obtenidos utilizando un protocolo simple de muestreo produjeron estimaciones de la abundancia de especies de mariposas comparables con los datos recolectados por medio de métodos estandarizados de monitoreo. Aunque se recomienda precaución en la extrapolación a partir de este estudio del Reino Unido de un número pequeño de insectos comunes y conspicuos, encontramos que la participación masiva en la ciencia ciudadana puede contribuir simultáneamente a la participación pública y al monitoreo de la biodiversidad. La ciencia ciudadana con participación masiva no es un remplazo adecuado para el monitoreo estandarizado de la biodiversidad pero puede extenderla y complementarla (p. ej.: por medio del muestreo de diferentes tipos de uso de suelo).

Camera trapping is a powerful tool for studying mammal populations over large spatial scales. Density estimation using camera-trap data is a commonly desired outcome, but most approaches only work for species that can be individually recognized, and researchers studying most mammals are typically constrained to measures of site occupancy or detection rate. These 2 metrics are often used as measures of relative abundance and presumed to be related directly to animal density. To test this relationship, we estimated density, occupancy, and detection rate of male white-tailed deer (Odocoileus virginianus) using camera-trap data collected from 1,199 cameras across 20 study sites. Detection rate and density exhibited stronger positive linear correlation (r2 = 0.80) than occupancy and density (r2 = 0.27). When hunted and unhunted paired areas were compared, detection rate and density showed the same trend between paired sites 62.5% of the time compared to 87.5% for occupancy and density. In particular, agreement between estimates was lowest for pairs of sites that had the largest differences in surrounding housing density. Although it is clear occupancy and detection rate contain some information about density, models suggested different ecological relationships associated with the metrics. Using occupancy or detection rate as proxies for density may be particularly problematic when comparing between areas where animals might to move or behave differently, such as urban–wild interfaces. In such cases, alternate methods of density approximation are recommended.

Counts of independent photo events from camera traps are commonly used to make inference about species occupancy, the density of unmarked populations, and the relative abundance of species across time and space. These applications rest on the untested assumption that data collected from individual cameras are representative of the landscape location in which they are placed, and that nearby cameras would record similar data when any additional micro‐site differences are accounted for. We established a high‐density camera trapping grid (100 × 100 m; 27 cameras) in Virginia, USA, to explicitly test these assumptions, investigating variation in capture rates and detection probabilities for a range of terrestrial mammals during four 2‐month seasonal surveys. Despite controlling for numerous habitat and placement factors, we documented, across all 5 focal species, large ranges and coefficients of variation in both capture rate and detection probabilities, which were similar to those seen across 2 sets of independent forest sampling sites from a larger, more typical camera trap sampling design. We also documented a lack of spatial autocorrelation in capture rate at any distance. Measured local covariates relevant to the camera viewshed (stem density, camera height, log presence, effective detection distance [EDD], total dbh of oak trees) rarely explained any significant portion of observed variation in capture rates or detection probabilities across the grid. The influence of EDD, measured here for the first time for individual camera stations, was inconsistently important and varied in direction of effect depending on species and season. Our study indicates single‐camera stations may fail to sample animal presence and frequency of use in a robust and repeatable way, primarily resulting from the influence of both idiosyncrasies in animal movement and measured and unknown micro‐site characteristics. We recommend spatial replication within sites (e.g., small‐scale shifting of cameras or use of multiple stations) should be considered to minimize impacts of relevant micro‐site characteristics, some of which may be difficult to identify.

The Virginia opossum (Didelphis virginiana), North America's only marsupial, has a range extending from southern Ontario, Canada, to the Yucatan Peninsula, Mexico, and from the Atlantic seaboard to the Pacific. Despite the Virginia opossum's taxonomic uniqueness in relation to other mammals in North America and rapidly expanding distribution, its ecology remains relatively understudied. Our poor understanding of the ecology of this important mesopredator is especially pronounced in the rural southeastern United States. Our goal was to estimate effects of habitat on opossum density within an extensive multi-year spatial capture-recapture study. Additionally, we compared the results of this spatial capture-recapture analysis with a simple relative abundance index. Opossum densities in the relatively underdeveloped regions of the southeastern United States were lower compared to the more human-dominated landscapes of the Northeast and Midwest. In the southeastern United States, Virginia opossums occurred at a higher density in bottomland swamp and riparian hardwood forest compared to upland pine (Pinus spp.) plantations and isolated wetlands. These results reinforce the notion that the Virginia opossum is commonly associated with land cover types adjacent to permanent water (bottomland swamps, riparian hardwood). The relatively low density of opossums at isolated wetland sites suggests that the large spatial scale of selection demonstrated by opossums gives the species access to preferable cover types within the same landscape.

Supralittoral zones of 13 sandy beaches of remote Rutland Island were divided into three zones to identify the litter contamination, its source, pathway of plastic transport to determine the level of macro-litter contamination, and its impact on coastal biota. Owing to the floral and faunal diversity, apart of the study area is protected under Mahatma Gandhi Marine National Park (MGMNP). The supralittoral zones of each sandy beach (between low-tide and high-tide line) were individually calculated from 2021 Landsat-8 satellite imagery before conducting the field survey. The total area of the surveyed beaches was 0.52 km 2 (5,20,020.79 m 2 ), and 317,565 litters representing 27 distinct litter types were enumerated. Two beaches in Zone-II and six in Zone-III were clean; however, all five in Zone-I were very dirty. The highest litter density (1.03 items/m 2 ) was observed in Photo Nallah 1 and Photo Nallah 2, whereas the lowest (0.09 items/m 2 ) was observed in Jahaji Beach. According to the Clean Coast Index ( CCI ), Jahaji Beach (Zone-III) is the very cleanest beach (1.74) while other beaches of Zone-II and Zone-III are clean. The findings of the Plastic Abundance Index ( PAI ) indicate that Zone-II and Zone-III beaches have a low abundance of plastics (< 1), while two beaches of Zone-I, viz., Katla Dera and Dhani Nallah, exhibited a moderate abundance of plastics (< 4) while a high abundance of plastics (< 8) was observed in the rest of three beaches of the same zone. The primary contributor of litter on Rutland’s beaches was plastic polymers (60–99%), which were presumed to originate from the Indian Ocean Rim Countries (IORC). A collective litter management initiative by the IORC is essential in preventing littering on remote islands.

Marine plastic debris has emerged as a pressing concern along the northern coast of Jaffna, Sri Lanka, posing a significant threat to marine resources. A preliminary study was conducted to investigate the abundance and characteristics of marine plastic debris at four major fish landing sites in the northern coast of Jaffna, using the Clean Coast Index (CCI) and Plastic Abundance Index (PAI). The results revealed that the average abundance of marine debris and plastic debris were 1.71 ± 0.42 items/m 2 and 1.66 ± 0.57 items/m 2 , respectively. The most common types of plastic debris represented plastic rope and net pieces (23.2%), followed by unidentified weathered plastic fragments (16.7%), beverage bottles (16.2%), bottle caps and lids (13%), and styrofoam (14.1%). The recognized sources of plastic debris were mainly fishing-based activities, recreation activities, transboundary sources, and unidentified sources. The fish landing sites were classified as exceptionally polluted, with a CCI exceeding 10 and a PAI value exceeding 8. Myliddy had the highest debris density, indicating substantial pollution levels, followed by Point Pedro, Mathagal, and Valveddithurai. These findings underscore the urgency of establishing a comprehensive plastic waste management framework for the northern coast of Jaffna and devising strategies to address buoyant debris within the northern Indian Ocean. Furthermore, this study carries significant implications for the local marine ecosystem, coastal communities, and Sri Lanka's broader environmental policies and practices.

Citizen-science data are increasingly used to contribute to our understanding of biodiversity change, but analysing such data requires suitable statistical methods, often to deal with forms of bias. We develop a new approach for modelling data from a snapshot, mass-participation citizen-science scheme for UK butterflies, the Big Butterfly Count (BBC). Butterfly abundance varies throughout the year as one or more generations of each species emerge and die off, and the timing (phenology) of emergences varies annually due to weather and climate. Thus, counts from the short 3-week BBC sampling period are susceptible to bias due to this inter-annual variation in phenology. We adapt the Generalised Abundance Index, drawing upon phenology estimates from standardised monitoring scheme data, to account for phenological bias in the estimation of species’ abundance trends from BBC data. The method is demonstrated via application to empirical and simulated data, revealing that not accounting for phenology leads to biased trend estimates, particularly for summer-flying single-generation species. Drawing upon phenology information, the new approach allows for the reporting of abundance trends from a snapshot citizen-science scheme, creating the potential to maximise available data sources to increase our understanding of changes in butterfly populations, particularly in urban environments.