Abundance Estimates Research Articles

Atmospherix

Measuring the abundances of C- and O-bearing species in exoplanet atmospheres enables us to constrain the C/O ratio, which contains indications about the planet formation history. With a wavelength coverage going from 0.95 to 2.5 µm, the high-resolution (R ~ 70 000) spectropolarimeter SPIRou can detect spectral lines of major bearers of C and O in exoplanets. Here, we present our study of SPIRou transmission spectra of WASP-76 b acquired for the ATMOSPHERIX programme. We applied the publicly available data analysis pipeline developed within the ATMOSPHERIX Consortium, analysing the data using 1-D models created with the petitRADTRANS code, with and without a grey cloud deck. We report the detection of H2O and CO at a Doppler shift of around −6 km s−1, which is consistent with previous observations of the planet. Finding that a deep cloud deck is favoured, we measured a mass mixing ratio (MMR) log(H2O)MMR = −4.52 ± 0.77 and log(CO)MMR = −3.09 ± 1.05 consistent with a sub-solar metallicity to more than 1σ. We report 3σ upper limits for the abundances of C2H2, HCN, and OH. We estimated a C/O ratio of 0.94 ± 0.39 (~ 1.7 ± 0.7 × solar, with errors indicated corresponding to the 2σ values) for the limbs of WASP-76 b at the pressures probed by SPIRou. We used 1-D ATMO forward models to verify the validity of our estimation. Comparing them to our abundance estimations of H2O and CO, as well as our upper limits for C2H2, HCN and OH, we found that our results were consistent with a C/O ratio between 1 and 2 × solar, and hence with our C/O estimation. Finally, we found indications of asymmetry for both H2O and CO when investigating the dynamics of their signatures, pointing to a complex scenario possibly involving both a temperature difference between limbs and the presence of clouds being behind the asymmetry that this planet is best known for.

When to monitor or control: Informed invasive species management using a partially observable Markov decision process (POMDP) framework

Abstract Resource allocation for invasive species management requires information about the size of the invasive population, which may be expensive and time‐consuming to obtain. The trade‐off between investment in monitoring and control efforts is a challenging decision problem, and existing mathematical tools are often difficult to interpret, and/or limited to a specific case study. We propose a partially observable Markov decision process (POMDP) framework to help decision‐makers understand effective monitoring and control policymaking. POMDPs can deal with uncertainty in both the model and state of the system but are more challenging to solve due to the continuous and high‐dimensional state space. Rather than limiting the possible states of the system, as do most previously proposed methods, we work through the development of a density projection approach, which reduces the dimensionality of the space of beliefs by restricting them to a parametrised family of probability distributions. This serves to align the mathematical representation of the problem with the real‐world quantities relevant to human decision‐making. The result of our model is a sequence of actions, which minimises the expected cost incurred in managing the invasive species, where the recommendation depends on an estimate of the species' abundance, and the uncertainty in this estimate. We demonstrate the effectiveness of our proposed framework with a case study on tropical fire ant (Solenopsis geminata) control and two generic case studies of varying complexity. Furthermore, we investigate sensitivity of the results to the choices of control cost and efficacy, and monitoring cost and error. The framework proposed by this paper makes the powerful machinery of POMDPs available to environmental managers. It computes the optimal course of action to manage a growing population of an invasive species, incorporating a varying time horizon and multiple control interventions. We sidestep the computational difficulties of general POMDPs to provide a clear, visual overview of decision‐making recommendations, and how these decisions change in new situations. Initial results and scenario‐based analysis show promising results, and the framework could be extended to the related field of disease management.

Quantifying the abundance and survival rates of island-associated spinner dolphins using a multi-state open robust design model

Spinner dolphins (Stenella longirostris subsp.) occupy the nearshore waters of several Hawaiian Islands. Due to their constrained behavioral pattern and genetic isolation, they are vulnerable to anthropogenic threats. Their occurrence and behavior are well-described, yet a lack of data on their abundance and survival rates hinders optimal conservation action. Using design-based photo-identification surveys, this study estimated the abundance, apparent survival, and emigration of spinner dolphins off the Waiʻanae Coast of Oʻahu through multi-state open robust design (MSORD) and POPAN modelling. Eight seasonal field seasons, (two winter, spring, summer, and autumn) each comprised of six surveys of the study area, were completed during two consecutive years. Seasonal abundance estimates derived from the best fitting model ranged from 140 (± 36.8 SE, 95% CI 84–232) to 373 (± 60.0, 95% CI 273–509) individuals and were lowest during winter seasons. The MSORD estimated a survival rate of 0.95 (± 0.02 SE) and a Markovian pattern of temporary emigration. POPAN modelling estimated a super-population size of 633 (± 78 SE, 95% CI 492–798), reflecting the total number of individual dolphins that used the study area during the entire study period. Additional research on circum- and inter-island dolphin movements around and between Oʻahu and the Maui Nui region may shed light on both seasonal movement patterns and overall abundance for the Oʻahu/4-Islands stock. This work represents the first systematic mark-recapture effort to assess the abundance and survival rates of these highly exposed dolphins, providing valuable insights for conservation and management.

Back from the brink: Estimating daily and annual abundance of natural-origin salmon smolts from 30-years of mixed-origin capture-recapture data

Evaluating the status and trends of natural-origin anadromous fish populations over time requires robust estimates of out-migrating juvenile abundance. Information on abundance is typically acquired by capturing actively migrating fish as they pass stationary monitoring platforms. Challenges to estimation include protracted migration timing, temporally varying capture probabilities and the contemporaneous presence of unmarked hatchery-origin fish. The confounding effects of unmarked hatchery fish are especially pernicious in systems hosting multiple hatchery programs with variable mark-rates among releases. Here, we address this problem for a regionally and culturally important population of Chinook salmon (Oncorhynchus tshawytscha) supported by a hatchery-supplementation program implemented in response to the listing of this population under the U.S. Endangered Species Act. We developed a model to estimate daily and annual abundance of naturally produced age-0 fall Chinook salmon passing Lower Granite Dam (Snake River, USA) for each of the last 30 years. We accounted for variable hatchery marking rates by integrating two related data sources: 1) release-recapture data of fish with individually identifiable tags and 2) counts of marked and unmarked sample of fish captured each day. We fit joint parameters for daily fish arrival and capture probabilities to these data to estimate the daily abundance of hatchery- and natural-origin fish. Our results show that from 1992 to 2021, the annual abundance of juvenile natural-origin Snake River fall Chinook salmon increased by two orders of magnitude. These results are the first comprehensive evaluation of multi-decadal trends in abundance and run-timing for this population. Our approach can be adapted to other runs and locations within the Columbia River basin or similar systems where out-migrating fish are monitored at fixed locations.

Modeling abundance and nest occupation of Pseudomyrmex termitarius as a function of detection factors and habitat structure in fragments of urban vegetation

Understanding the relationship between abundance and habitat variability is an underlying theme of ecological studies. Due to imperfect detection, robust estimates of population size require large efforts, especially for megadiverse taxa such as Formicidae, where most of the surveys rely on presence-absence or community data. Here, we used mixed occupation models to estimate the population abundance and local distribution of Pseudomyrmex termitarius (Formicidae: Pseudomyrmecinae) in nests when detection was imperfect. Our goal was to create a sampling protocol to estimate unbiased population parameters, taking into account several sources of heterogeneity that can affect the estimates (e.g., researcher detection variability, survey period, habitat variables). The advantage of this protocol is that it is time- and cost-effective for accessing common and easily identifiable ant species. We distributed 44 transects (2.5 × 20 m) with five subplots each in 2.33 ha of the vegetation remnants of the Cuiabá Campus of the Federal University of Mato Grosso (UFMT). In each subsample, we recorded the presence or absence of P. termitarius workers, as well as the count of other cooccurring ant species during two consecutive days and time periods (midday and 16:00). Canopy openness and soil compaction were recorded in each transect. Among all the sampling trials, we directly counted a maximum of 364 P. termitarius workers in 92 subplots. Both nest abundance and occupation estimates were directly related to soil compaction. The estimated probability of detection was 39% and was not affected by the period. On the other hand, despite P. termitarius being a noncryptic species in the study area, it had a stronger effect on detectability. Although P. termitarius is a subordinate species, the co-occurrence of other species does not explain its abundance and distribution in our study. Perhaps P. termitarius's preference for sites with more compact soils could favor it by filtering out or reducing the abundance of potential competitors. After taking into account soil compaction and researcher detection variability, we estimated that the species occurred in 70% of the study area with a density of approximately 0.09 nests/m². We expect that this protocol can be an additional tool to improve the knowledge of ants in general and, especially, the management of alien or economically relevant ant species.

Can hair snares provide a reliable method for estimating abundance of an exotic ungulate?

Mitigating exotic species invasions requires reliable information on abundance and source-sink dynamics to evaluate effectiveness of control efforts. Despite being present in the southeastern United States for centuries, wild pigs (Sus scrofa) continue to invade new areas, including the Sam D. Hamilton Noxubee National Wildlife Refuge (NNWR) in Mississippi, a forested landscape with an emerging wild pig population. Considering the early stage of invasion, we designed and implemented a non-invasive hair sampling technique to estimate abundance and relatedness of wild pigs within the NNWR. Following 8-week sampling periods in 2021 (n = 160 samples) and 2022 (n = 159 samples), we identified 38 and 26 unique individuals, respectively, although recapture rates were too low to yield precise abundance estimates. We also identified low heterozygosity indicating high relatedness among our population which likely reflected lack of barrier features to gene flow and/or a limited number of external source populations. Finally, we observed positive trends in viable samples collected relative to sampling week, suggesting more optimal sampling period(s) may exist (e.g., autumn). We also acknowledge that use of grain in more accessible areas rather than our remote approach using scent-only attractants may increase hair snare attractiveness and wild pig encounters, while permitting increased sampling frequency. Collectively, these modifications (i.e., altered season, bait type, and access) provide viable pathways to increase precision for abundance estimates. When used concomitantly with ongoing monitoring and control efforts, non-invasive hair sampling will contribute to a more holistic understanding of this exotic species and better inform management actions.

Deep Cell-Type Deconvolution from Bulk Gene Expression Data Using DECODE

It is becoming clear that bulk gene expression measurements represent an average over very different cells. Elucidating the expression and abundance of each of the encompassed cells is key to disease understanding and precision medicine approaches. A first step in any such deconvolution is the inference of cell type abundances in the given mixture. Numerous approaches to cell-type deconvolution have been proposed, yet very few take advantage of the emerging discipline of deep learning and most approaches are limited to input data regarding the expression profiles of the cell types in question. Here we present DECODE, a deep learning method for the task that is data-driven and does not depend on input expression profiles. DECODE builds on a deep unfolded non-negative matrix factorization technique. It is shown to outperform previous approaches on a range of synthetic and real data sets, producing abundance estimates that are closer to and better correlated with the real values.

The implications of estimating rarity in Brazilian reptiles from GBIF data based on contributions from citizen science versus research institutions

AbstractUnderstanding the distribution of rare species is important for conservation prioritisation. Traditionally, museums and other research institutions have served as depositories for specimens and biodiversity information. However, estimating abundance from these sources is challenging due to spatiotemporally biased collection methods. For instance, large‐bodied reptiles that are found near research institutions or in popular, easily accessible sites tend to be overrepresented in collections compared to smaller species found in remote areas. Recently, a substantial number of observations have been amassed through citizen (or community) science initiatives, which are invaluable for monitoring purposes. Given the unstructured nature of this sampling, these datasets are often affected by biases, such as taxonomic, spatial and temporal preferences. Therefore, analysing data from these two sources can lead to different abundance estimates. This study compiled data on Brazilian reptile species from the Global Information Biodiversity Facility (GBIF). It employed a community‐ecology approach to analyse data from research institutions and citizen science initiatives, separately and collectively, to assess taxonomic and spatial species coverage and predict species rarity. Using a 1‐degree hexagonal grid, we analysed the spatial distribution of reptile communities and calculated rarity indices for 754 reptile species. Our findings reveal that 87 species were exclusively recorded in the citizen science subset, while 212 were recorded only by research institutions. The number of observations per species in the citizen science data followed a Gambin distribution, which aligns with the expected pattern of abundance in natural communities, unlike the data from research institutions. This suggests that citizen science data may be a more accurate source for estimating species abundance and rarity. The discrepancies in rarity classifications between the datasets were likely due to differences in sample size and potentially other sampling parameters. Nevertheless, combining data collected by both research institutions and citizen science initiatives can help to fill knowledge gaps in reptile species occurrence, thus enhancing the foundation for conservation efforts on a national scale.

Model-based indices of juvenile Pacific salmon abundance highlight species-specific seasonal distributions and impacts of changes to survey design

Changes in survey implementation can bias traditional design-based indices of abundance, particularly for migratory species where local abundance can vary at short temporal scales. Spatiotemporal model-based estimators provide a flexible alternative that can better account for changes in the timing and location of surveys. Here we develop a geostatistical model, a generalized linear mixed effects model with Gaussian Markov random fields, that is sufficiently flexible to account for changes in survey design, as well as seasonal and interannual variability in abundance and spatial distribution. We then apply this model to a suite of migratory species—juvenile Pacific salmon—with survey data collected from southern British Columbia’s continental shelf over the previous 25 years. Juvenile Pacific salmon showed species-specific spatial distributions with considerable seasonal variability. While the location of species-specific hot spots varied among years, seasonal variation in distributions were greater than interannual variation. We found that a shift from standard transects and opportunistic sampling to a random stratified survey design resulted in lower estimates of abundance for four of five juvenile Pacific salmon species; however, these differences were not significant after accounting for other sampling attributes. Trends in abundance for several species shifted when models included survey and diel effects. Our approach provides a means of deriving indices of abundance for migratory species from surveys with variable effort in time and space. Ultimately such indices can be used to better understand the ecological mechanisms regulating productivity by linking distribution and abundance to environmental drivers.

Levels of 5α-reductase gene in intestinal lavage fluid decrease with progression of colorectal cancer.

Introduction. Colorectal cancer (CRC) is a leading cause of cancer deaths, closely linked to the intestinal microbiota and bile acid metabolism. Secondary bile acids, like deoxycholic and lithocholic acid, are associated with increased CRC risk due to their disruption of vital cellular functions. In contrast, isoallolithocholic acid (isoalloLCA) shows potential health benefits, highlighting the complex role of bile acids in CRC. A specific primer set was previously developed to amplify homologs of the 5α-reductase gene (5ar), which are involved in the biosynthesis of isoalloLCA, thereby enabling the estimation of abundance of 5ar (5ar levels) in the intestine.Hypothesis/Gap Statement. We hypothesized that 5ar levels in the intestine are associated with CRC.Aim. This study aimed to investigate intestinal 5ar levels and compare them across different stages of the adenoma-carcinoma sequence, providing insights into novel strategies for monitoring CRC risk.Methodology. DNA was extracted from intestinal lavage fluids (ILF) collected during 144 colonoscopies. Next-generation sequencing (NGS) was employed to examine the sequence of 5ar homologues, using a specific primer set on DNA from seven selected ILFs - four from carcinoma patients and three from individuals with non-neoplastic mucosa. Additionally, we used quantitative PCR (qPCR) to measure 5ar levels in all 144 DNA samples.Results. We conducted 144 colonoscopies and categorized patients according to the adenoma-cancer sequence: 52 with non-neoplastic mucosa, 69 with adenomas and 23 with carcinoma. Analysis of 292,042 NGS-derived 5ar sequences revealed the seven most prevalent amplicon sequence variants, each 254 base pairs in length. These closely matched or were identical to 5ar sequences in Bacteroides uniformis, Phocaeicola vulgatus and Phocaeicola dorei. Furthermore, qPCR analysis demonstrated significantly lower 5ar levels in the carcinoma group compared to those in the non-neoplastic mucosa group (P = 0.0004). A similar, though not statistically significant, trend was observed in the adenoma group (P = 0.0763), suggesting that 5ar levels decrease as CRC progresses.Conclusion. These findings indicate that PCR-based monitoring of 5ar levels in intestinal samples over time could provide a non-invasive, rapid and cost-effective method for assessing an increased risk of CRC.

Empirical calibration for helium abundance determinations in active galactic nuclei

ABSTRACT For the first time, a calibration between the He i$\lambda 5876$/H $\beta$ emission line ratio and the helium abundance y = 12 + log(He/H) for Narrow line regions of Seyfert 2 Active Galactic Nuclei is proposed. In this context, observational data (taken from the SDSS-DR15 and from the literature) and direct abundance estimates (via the $T_{\rm e}$-method) for a sample of 65 local ($z \: \lt \: 0.2$) Seyfert 2 nuclei are considered. The resulting calibration estimates the y abundance with an average uncertainty of 0.02 dex. Applying our calibration to spectroscopic data containing only strong emission lines, it yields a helium abundance distribution similar to that obtained via the $T_{\rm e}$-method. Some cautions must be considered to apply our calibration for Seyfert 2 nuclei with high values of electron temperature (${\gtrsim} 20\, 000$ K) or ionization parameter ($\log U \ \gt\ {-}2.0$).

Density and abundance estimates of cetaceans in the Black Sea through aerial surveys (ASI/CeNoBS)

IntroductionPopulation abundance is amongst the most basic and crucial parameters for the assessment of conservation status of any species. Three species of odontocetes, all represented by local subspecies, inhabit the Black Sea: the Black Sea common dolphin Delphinus delphis ponticus, the Black Sea bottlenose dolphin Tursiops truncatus ponticus, and the Black Sea harbour porpoise Phocoena phocoena relicta. Their populations are threatened by multiple factors, including overfishing of their prey, bycatch, pollution and epizootics. Despite this, there are no basinwide estimates for any cetacean species in the Black Sea.MethodsIn 2019, a systematic study was carried out under the EU CeNoBS project. Six strata were designed in the waters of Bulgaria, Georgia, Romania, Türkiye and Ukraine, covering most of territorial and offshore waters, which were surveyed between June 19 and July 4. A line transect distance sampling approach was used, following predefined transects within each stratum, achieving a 5% coverage of the surveyed area. A total of 7,344 kilometres of transects were surveyed recording a total of 1,744 cetacean sightings. Design-based abundance estimates were obtained using a Multiple Covariate Distance Sampling (MCDS) approach. Model-based abundance estimates were also derived using a Generalized Additive Models (GAM) approach, linking species sightings with a number of environmental covariates (e.g., bathymetric features, sea surface temperature, chlorophyll-a) over a grid of 10x10 km.Results and discussionThe uncorrected (for perception and availability bias) estimates obtained through the model-based analysis were 108,283 (CV=0.07) common dolphins, 22,720 (CV=0.15) bottlenose dolphins and 93,808 (CV=0.06) harbour porpoises. These aerial surveys yielded the first insights on overall abundance, density and distribution, providing current regional baseline values and density maps for all three cetacean species of the Black Sea during the summer months, to be used for the elaboration of effective conservation measures and to address national and international requirements.

Abundance of humpback whales in the eastern Indian sector of the Southern Ocean in 2018/19 using opportunistic sighting survey data with a note on the occurrence of other cetaceans

Past commercial whaling depleted the humpback whale in the Southern Ocean; however, its population has recovered since hunting was terminated in 1966. Information on abundance is essential not only managing target animals but understanding the ecosystem because the species is one of the main consumers of Antarctic krill. Abundance of this species has not been reported in the eastern Indian sector (80–150°E) of the Southern Ocean since the mid-2000s. A multidisciplinary ecosystem survey focusing on Antarctic krill was conducted in the area by the Japanese research vessel Kaiyo-maru in 2018/19 austral summer. An opportunistic cetacean sighting survey (platform of opportunity (POP) survey) was conducted as part of the survey. Four baleen and five toothed whale species were sighted during the sighting survey. Humpback whales (113 schools/178 animals) were most frequently observed, followed by fin whales (15/36). Abundance of humpback whales was estimated as 22,395 individuals (CV = 0.270, 95 % confidence intervals: 13,310–37,663) based on the design-based line transect method. Krill consumption by humpback whales in this area was tentatively estimated as 1.336 million tons, corresponding to 30.9 % of krill biomass, although the amount varied greatly depending on the consumption rate’s assumption. The estimated abundance was comparable with previous estimates, however, there are some caveats in interpreting the results. As no approaching whale sightings were made during this POP survey, the proportion of sightings of unidentified large whales was high, possibly leading to an underestimation of the abundance. Dedicated sighting surveys are desirable to obtain more accurate and unbiased abundance estimates, especially for management purposes, although the results of this study could be used for ecological studies in combination with other data sets recoded by the multidisciplinary survey.

A multispecies hierarchical model to integrate count and distance‐sampling data

AbstractIntegrated community models—an emerging framework in which multiple data sources for multiple species are analyzed simultaneously—offer opportunities to expand inferences beyond the single‐species and single‐data‐source approaches common in ecology. We developed a novel integrated community model that combines distance sampling and single‐visit count data; within the model, information is shared among data sources (via a joint likelihood) and species (via a random‐effects structure) to estimate abundance patterns across a community. Parameters relating to abundance are shared between data sources, and the model can specify either shared or separate observation processes for each data source. Simulations demonstrated that the model provided unbiased estimates of abundance and detection parameters even when detection probabilities varied between the data types. The integrated community model also provided more accurate and more precise parameter estimates than alternative single‐species and single‐data‐source models in many instances. We applied the model to a community of 11 herbivore species in the Masai Mara National Reserve, Kenya, and found considerable interspecific variation in response to local wildlife management practices: Five species showed higher abundances in a region with passive conservation enforcement (median across species: 4.5× higher), three species showed higher abundances in a region with active conservation enforcement (median: 3.9× higher), and the remaining three species showed no abundance differences between the two regions. Furthermore, the community average of abundance was slightly higher in the region with active conservation enforcement but not definitively so (posterior mean: higher by 0.20 animals; 95% credible interval: 1.43 fewer animals, 1.86 more animals). Our integrated community modeling framework has the potential to expand the scope of inference over space, time, and levels of biological organization, but practitioners should carefully evaluate whether model assumptions are met in their systems and whether data integration is valuable for their applications.

Constraining the helium-to-metal enrichment ratio deltaY/deltaZ from main-sequence binary stars. Theoretical analysis of the accuracy and precision of the age and helium abundance estimates

We aim to investigate the theoretical possibility of accurately determining the helium-to-metal enrichment ratio $ Y/ Z$ from precise observations of double-lined eclipsing binary systems. Using Monte Carlo simulations, we drew synthetic binary systems with masses between 0.85 and 1.00 $M_ sun $ from a grid of stellar models. Both stars were sampled from a grid with $ Y/ Z = 2.0$, with a primary star at 80<!PCT!> of its main-sequence evolution. Subsequently, a broader grid with $ Y/ Z$ from 1.0 to 3.0 was used in the fitting process. To account for observational uncertainties, two scenarios were explored: S1, with realistic uncertainties of 100 K in temperature and 0.1 dex in Fe/H ; and S2, with halved uncertainties. We repeated the simulation at two baseline metallicities: Fe/H = 0.0 and $-0.3$. The posterior distributions of $ Y/ Z$ revealed significant biases. The distributions were severely biased towards the edge of the allowable range in the S1 error scenario. The situation only marginally improved when considering the S2 scenario. The effect is due to the impact of changing $ Y/ Z$ in the stellar effective temperature and its interplay with Fe/H observational error, and it is therefore not restricted to the specific fitting method. Despite the presence of these systematic discrepancies, the age of the systems were recovered unbiased with 10<!PCT!> precision. Our findings indicate that the observational uncertainty in effective temperature and metallicity significantly hinders the accurate determination of the $ Y/ Z$ parameter from main-sequence binary systems.

The 2024 BBN baryon abundance update

We revisit the state of the light element abundances from big bang nucleosynthesis in early 2024 with particular focus on the derived baryon abundance. We find that the largest differences between the final baryon abundances are typically driven by the assumed Deuterium burning rates, characterized in this work by the underlying code. The rates from theoretical ab-initio calculations favor smaller baryon abundances, while experimentally-determined rates prefer higher abundances. Through robust marginalization over a wide range of nuclear rates, the recently released PRyMordial code allows for a conservative estimate of the baryon abundance at Ω bh 2 = 0.02218 ± 0.00055 (using PDG-recommended light element abundances) in ΛCDM and Ω bh 2 = 0.02196 ± 0.00063 when additional ultra-relativistic relics are considered (ΛCDM + N eff). These additional relics themselves are constrained to ΔN eff = -0.10 ± 0.21 by light element abundances alone.

Tick abundance and infection with three zoonotic bacteria are heterogeneous in a Belgian peri-urban forest.

Ixodes ricinus is a vector of several pathogens of public health interest. While forests are the primary habitat for I. ricinus, its abundance and infection prevalence are expected to vary within forest stands. This study assesses the spatio-temporal variations in tick abundance and infection prevalence with three pathogens in and around a peri-urban forest where human exposure is high. Ticks were sampled multiple times in 2016 and 2018 in multiple locations with a diversity of undergrowth, using the consecutive drags method. Three zoonotic pathogens were screened for, Borrelia burgdorferi s.l., Coxiella burnetii, and Francisella tularensis. The influence of season, type of site and micro-environmental factors on tick abundance were assessed with negative binomial generalized linear mixed-effects models. We collected 1642 nymphs and 181 adult ticks. Ticks were most abundant in the spring, in warmer temperatures, and where undergrowth was higher. Sites with vegetation unaffected by human presence had higher abundance of ticks. Forest undergrowth type and height were significant predictors of the level of tick abundance in a forest. The consecutive drags method is expected to provide more precise estimates of tick abundance, presumably through more varied contacts with foliage. Borrelia burgdorferi s.l. prevalence was estimated from pooled ticks at 5.33%, C. burnetii was detected in six pools and F. tularensis was not detected. Borrelia afzelii was the dominant B. burgdorferi genospecies. Tick abundance and B. burgdorferi s.l. infection prevalence were lower than other estimates in Belgian forests.

Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay

AbstractObjectiveOnce thought to be extirpated from the Chesapeake Bay, fall spawning runs of Atlantic Sturgeon Acipenser oxyrinchus have been rediscovered in the Marshyhope Creek (MC)–Nanticoke River (NR) system of Maryland, United States. High recapture rates in past telemetry surveys suggested a small population in the two connected tributaries. This study aims to generate estimates of abundance and understand within system connectivity for spawning runs in 2020 and 2021.MethodsData from mobile side‐scan sonar surveys and detections of acoustically tagged adults on stationary telemetry receivers were analyzed in an integrated model to estimate spawning season abundance and examine run timing and system connectivity for this population. An array of acoustic receivers was deployed throughout the MC–NR system to monitor the movement of tagged fish during the spawning run period from mid‐August to late October. Side‐scan sonar surveys were conducted weekly in September in an area of high spawner aggregation to generate count data on spawning run abundance.ResultIn 2020 and 2021, 32 (95% credible interval [CRI] = 23–47) and 70 (95% CRI = 49–105) Atlantic Sturgeon, respectively, used the MC–NR system. The lower estimate for 2020 coincided with an earlier end to the spawning run related to cooler September temperatures in that year.ConclusionIn both years, high spawning run connectivity between MC and the upper NR was observed. Overall, run estimates supported previous hypotheses that the MC–NR system supports a very small population and that both MC and the upper NR serve as important areas for spawning activity.

Graph feature fusion driven by deep autoencoder for advanced hyperspectral image unmixing

In this paper, we propose a pioneering approach for blind Hyperspectral Image (HSI) unmixing named Multi-Features Graph Deep Fusion Learning Networks for HSI Unmixing (MF-GDL). Our method leverages the power of multi-feature graph deep fusion learning networks. MF-GDL integrates spectral HSI graph features with complementary spatial graph features, overcoming the fixed window size limitation observed in prior deep learning approaches in capturing spatial relationships effectively. A key contribution lies in the introduction of multi-view graph learning, a novel aspect in HSI unmixing. By incorporating graph similarity-based spatial information, we address an important gap in the existing literature, marking the first attempt, to our knowledge, to tackle this challenge using a graph deep learning method. Furthermore, we introduce auxiliary information based on Extended Morphological Profiles (EMPs) to enrich spatial context beyond high-level spatial relationships between neighboring pixels. This multi-view fusion learning not only improves accuracy in material abundance estimation but also demonstrates the significance of considering multiple perspectives in HSI unmixing. We validate the efficacy of MF-GDL on four real datasets: Jasper Ridge, Samson, Urban, and Apex showcasing its robust performance and highlighting the substantial contribution of the multi-view aspect in advancing HSI unmixing techniques.

Wetland Seed Abundance Estimates Influenced by Soil Core Volume

Wetland Seed Abundance Estimates Influenced by Soil Core Volume