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46 results for “sampling effort”
Sampling Effort Metadata for the Central and South Atlantic Offshore and Deep-Sea Benthos
<p>Metadata containing information on the sampling of benthic taxa in => 30 m water depth in the Central and South Atlantic. This data was compiled as part of a baseline review of the science, policy and management of the region (Bridges et al. in press). Metadata was compiled from sources identified through a literature search and information provided by members of the Challenger 150 Central and South Atlantic Regional Scientific Research Working Group.</p> <p>Version 1 (November, 2022): Metadata used in the Bridges et al. (in press) gap analysis with the exclusion of sensitive datasets (Atkinson et al. In prep). These are in the process of being made open access and will be added in due course. </p> <p> </p> <p><strong>References</strong></p> <p>Atkinson et al in prep. SeaMap FBIP.</p> <p>Bridges. A.E.H., Howell, K.L., Amaro, T., Atkinson, L., Barnes, D.K.A., Bax, N., Bell, J.B., Bernardino, A.F., Beuck, L., Braga-Henriques, A., Brandt, A., Bravo, M.E., Brix, S., Butt, S., Carranza, A., Doti, B.L., Elegbede, I.O., Esquete, P., Freiwald, A., Gaudron, S.M., Guilhon, M., Hebbeln, D., Horton, T., Kainge, P., Kaiser, S., Lauretta, D., Limongi, P., Mcquaid, K.A., Milligan, R.J., Miloslavich, P., Narayanaswamy, B.E., Orejas, C., Paulus, S., Pearman, T.R.R., Perez, J.A., Ross, R.E., Saeedi, H., Shimabukuro, M., Sink, K., Stevenson, A., Taylor, M., Titschack, J., Vieira, R.P., Vinha, B. & Wienberg, C. Review of the Central and South Atlantic Shelf and Deep-Sea Benthos: Science, Policy and Management. <em>Oceanography and Marine Biology: An Annual Review</em>.</p> <p> </p>
Figure 3 in Ecological and reproductive parameters of the seabob shrimp, Xiphopenaeus spp. (Heller, 1862) on the southern coast of the state of Espírito Santo, Brazil: potential use of less sampling effort
Figure 3. Principal component analysis (PCA) for Xiphopenaeus spp. abundance and environmental variables in Anchieta region. The samples were collected between February/2013 and February/2015. Ab: Abundance; Gr: Granulometry; O.M: Organic Matter; Sal: Salinity: Temp: Temperature.
Figure 2 in Ecological and reproductive parameters of the seabob shrimp, Xiphopenaeus spp. (Heller, 1862) on the southern coast of the state of Espírito Santo, Brazil: potential use of less sampling effort
Figure 2. Boxplot of Xiphopenaeus spp. abundance at each collection point = transects, (A) and season (B) between February/2013 and February/2015. p1: Point 1; p2: Point 2; p3: Point 3. *Statistically significant difference.
Figure 6 in Ecological and reproductive parameters of the seabob shrimp, Xiphopenaeus spp. (Heller, 1862) on the southern coast of the state of Espírito Santo, Brazil: potential use of less sampling effort
Figure 6. Percentage values of gonadal development stages of Xiphopenaeus spp. at sampling points = transects, (A and B) and sampling period (C and D). Males (A and C) and females (B and D). Immature (IM), rudimentary (RU), developing (ED) and developed (DE) at each sampling point from February/2013 to February/2015. P1: Point 1, P2: Point 2, P3: Point 3.
Figure 1 in Ecological and reproductive parameters of the seabob shrimp, Xiphopenaeus spp. (Heller, 1862) on the southern coast of the state of Espírito Santo, Brazil: potential use of less sampling effort
Figure 1. Map of Brazil highlighting the state of Espírito Santo and the fishing port of Anchieta, indicating the sampling points of the seabob shrimp. (P1 = Point 1: 2m; P2 = Point 2: 5m; P3 = Point 3: 10m; blue line = Benevente River).
Figure 5 in Ecological and reproductive parameters of the seabob shrimp, Xiphopenaeus spp. (Heller, 1862) on the southern coast of the state of Espírito Santo, Brazil: potential use of less sampling effort
Figure 5. Frequency of the carapace size of the Xiphopenaeus spp. shrimp collected from February/2013 to February/2015. M: Male, F: Female, J: Juvenile. The dashed line indicates the LC50 as reference.
Figure 4 in Ecological and reproductive parameters of the seabob shrimp, Xiphopenaeus spp. (Heller, 1862) on the southern coast of the state of Espírito Santo, Brazil: potential use of less sampling effort
Figure 4. Carapace length (LC) of males (A) and females (B) upon reaching sexual maturity estimated by logistic regression based on the absence (0) or presence (1) of specific morphological sexual characters plotted as a function of carapace length (mm) of Xiphopenaeus spp. in Anchieta, southern coast of Espírito Santo, Brazil (LC50 = Length that 50% of individuals reach in adult size).
Piecewise continuous sampling: a method for minimizing bias and sampling effort for estimated metrics of animal behavior
<p>Capturing qualitative features of animal behavior requires recording occurrences of behavior over time. Continuous sampling is best for capturing brief behaviors, but can be very time consuming. Instantaneous sampling can reduce the amount of labor required, but can miss short-duration behaviors. We therefore synthesized these techniques by continuously sampling during randomly scattered time intervals; a technique we call piecewise continuous sampling. To optimize and test the efficacy of this technique, we collected a continuous behavioral dataset of harvester ant workers, and then we developed a protocol to estimate the amount of sampling time necessary to reconstruct the proportion of time animals spend in different behavioral states. This protocol finds the sample size needed for the variance of the sample to converge on the variation of the population. We then divided this estimated time into equal-duration intervals that were randomly distributed across the entire continuous dataset. Finally, we calculated both time-dependent and time-independent error from this sample. We found that 4 to 16 sampling intervals minimize both types of error simultaneously. This finding was robust to differences in underlying behavior and was validated with simulations, implying that this method could be used for many types of organisms.</p>
SSP: An R package to estimate sampling effort in studies of ecological communities
<p>SSP (simulation-based sampling protocol) is an R package that uses simulations of ecological data and dissimilarity-based multivariate standard error (MultSE) as an estimator of precision to evaluate the adequacy of different sampling efforts for studies that will test hypothesis using permutational multivariate analysis of variance. The procedure consists in simulating several extensive data matrixes that mimic some of the relevant ecological features of the community of interest using a pilot data set. For each simulated data, several sampling efforts are repeatedly executed and MultSE calculated. The mean value, 0.025 and 0.975 quantiles of MultSE for each sampling effort across all simulated data are then estimated and standardized regarding the lowest sampling effort. The optimal sampling effort is identified as that in which the increase in sampling effort does not improve the highest MultSE beyond a threshold value (e.g. 2.5 %). The performance of SSP was validated using real data. In all three cases, the simulated data mimicked the real data and allowed to evaluate the relationship MultSE – n beyond the sampling size of the pilot studies. SSP can be used to estimate sample size in a wide variety of situations, ranging from simple (e.g. single site) to more complex (e.g. several sites for different habitats) experimental designs. The latter constitutes an important advantage in the context of multi-scale studies in ecology. An online version of SSP is available for users without an R background.</p>
Figure. Observed (S obs) and estimated species richness for Chao 2, Jackknife 2, and Bootstrap, calculated for Lumbricidae in East Serbia. Vertical dashed lines represent 50%, 75%, and 100% of the sampling effort, respectively. in A nonparametric approach in quantifying species richness of Lumbricidae in East Serbia, Balkan Peninsula
Figure. Observed (S obs) and estimated species richness for Chao 2, Jackknife 2, and Bootstrap, calculated for Lumbricidae in East Serbia. Vertical dashed lines represent 50%, 75%, and 100% of the sampling effort, respectively.
Fig. 2 in Updating the distributions of four Uruguayan hylids (Anura: Hylidae): recent expansions or lack of sampling effort?
Fig. 2. Occurrence of Dendropsophus minutus (n = 15), D. nanus (n = 15), Lysapsus limellum (n = 6), and Scinax nasicus (n = 21) in different types of environments. Crops include rainfed crops, rice, sugar cane, and Eucalyptus and/or Pinus afforestations; Natural includes the grasslands, wetlands, and native forests with low anthropic influence (i.e., extensive livestock farming); and Urban refers to urban and peri-urban areas, routes, or industrial plants.
Fig. 1 in Updating the distributions of four Uruguayan hylids (Anura: Hylidae): recent expansions or lack of sampling effort?
Fig. 1. Distribution of Dendropsophus minutus, D. nanus, Lysapsus limellum, and Scinax nasicus in Uruguay. Shaded areas correspond to estimated distributions according to Carreira and Maneyro (2019, yellow), and the closest national protected areas (green). Black dots indicate previous literature records from Gudynas and Rudolf (1983), Langone and Basso (1987), Olmos et al. (1997), Kolenc et al. (2003), Núñez et al. (2004), and Prigioni et al. (2011). New records in the present study are indicated in red. Department names are indicated as follows: AR, Departamento de Artigas; SA, Departamento de Salto; PA, Departamento de Paysandú; RN, Departamento de Río Negro; CL, Departamento de Cerro Largo; and TT, Departamento de Treinta y Tres.
Fig. 1 in Sampling effort and fish species richness in small terra firme forest streams of central Amazonia, Brazil
Fig. 1. Fish species accumulation curves estimated from samples obtained in 1st, 2nd, and 3rd order streams reaches located in the study areas of Biological Dynamics of Forest Fragments Project, Manaus, Amazonas State. The curves represent extrapolations from five reaches sampled in each stream segment.
SSP: An R package to estimate sampling effort in studies of ecological communities
Open the record for dataset details and reuse information.
Piecewise continuous sampling: a method for minimizing bias and sampling effort for estimated metrics of animal behavior
Open the record for dataset details and reuse information.
Which pitfall traps and sampling effort to choose to evaluate cropping system effects on spider and carabid assemblages?
<p>Dataset and example of the script (R) used in the simulation approach.</p>
Figure 7 in Ecological and reproductive parameters of the seabob shrimp, Xiphopenaeus spp. (Heller, 1862) on the southern coast of the state of Espírito Santo, Brazil: potential use of less sampling effort
Figure 7. Cohen's power curve for evaluating the sample size of two samples.
Data from: When less is more and more is less: the impact of sampling effort on species delineation
Taxonomy is the very first step of most biodiversity studies, but how confident can we be in the taxonomic-systematic exercise? One may hypothesise that the more material, the better the taxonomic delineation, because the more accurate the description of morphological variability. As rarefaction curves assess the degree of knowledge on taxonomic diversity through sampling effort, we aim to test the impact of sampling effort on species delineation by subsampling a given assemblage. To do so, we use an abundant and morphologically diverse conodont fossil record. Within the assemblage, we first recognize four well established morphospecies but about 80% of the specimens share diagnostic characters of these morphospecies. We quantify these diagnostic characters on the sample using geometric morphometrics, and assess the number of morphometric groups, i.e. morphospecies, using ordination and cluster analyses. Then we gradually subsample the assemblage in two ways (randomly and by mimicking taxonomist work) and redo the 'ordination + clustering' protocol to appraise the evolution of the number of clusters related to sampling effort. We observe the number of delineated morphospecies decreasing when increasing the number of specimens, whatever the subsampling method, resulting mostly in less morphospecies than expected. Such rather counter-intuitive influence of sampling effort on species delineation highlights the complexity of taxonomical work. This indicates that new morphotaxa should not be erected based on small samples, and encourages researchers to largely illustrate, measure, and quantitatively compare their material to better constrain the morphological variability of a clade, and so to better characterize and delineate morphospecies. --
Data from: Effects of sampling effort on biodiversity patterns estimated from environmental DNA metabarcoding surveys
Environmental DNA (eDNA) metabarcoding can greatly enhance our understanding of global biodiversity and our ability to detect rare or cryptic species. However, sampling effort must be considered when interpreting results from these surveys. We explored how sampling effort influenced biodiversity patterns and nonindigenous species (NIS) detection in an eDNA metabarcoding survey of four commercial ports. Overall, we captured sequences from 18 metazoan phyla with minimal differences in taxonomic coverage between 18 S and COI primer sets. While community dissimilarity patterns were consistent across primers and sampling effort, richness patterns were not, suggesting that richness estimates are extremely sensitive to primer choice and sampling effort. The survey detected 64 potential NIS, with COI identifying more known NIS from port checklists but 18 S identifying more operational taxonomic units shared between three or more ports that represent un-recorded potential NIS. Overall, we conclude that eDNA metabarcoding surveys can reveal global similarity patterns among ports across a broad array of taxa and can also detect potential NIS in these key habitats. However, richness estimates and species assignments require caution. Based on results of this study, we make several recommendations for port eDNA sampling design and suggest several areas for future research.
Biases and distribution patterns in hard-bodied microscopic animals (Acari: Halacaridae): Size doesn't matter, but generalism and sampling effort do
<span>Aim</span> <p><span>The interplay between distribution ranges, species traits, and sampling and taxonomic biases remain elusive amongst microscopic animals. This ignorance obscures our understanding of the diversity patterns of a major component of biodiversity. Here, we used marine Halacaridae to explore whether differences between marine provinces can explain their distribution patterns or if differential sampling efforts across regions prevent any macroecological inference. Furthermore, we test if certain functional traits influence their distribution patterns.</span></p> <span>Location</span> <p><span>Europe.</span></p> <span>Results</span> <p><span>Whereas geographical variables provided a better explanation for differences in species composition, sampling effort and distance from marine biological stations accounted for the majority of differences in European Halacaridae richness. Species occurring in more habitats showed broader geographical ranges and accumulated more records. Species traits like body size affected the distribution of halacarid species.</span></p> <span>Main conclusions</span> <p><span>We propose that the sampling effort of halacarid mites in Europe might be explained by two different cognitive biases: the convenience of selecting certain sampling localities compared to others, and the tendency of zoologists to scrutinize habitats where their target organisms are more common.</span></p>
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