All of the above comparisons are based on studies where true density is established by studying a small number of habituated groups, and estimating the size of their home range. There are several reasons why there might be bias in these ‘true’ densities. For example home ranges of groups may partially overlap, and because the transects in these studies are positioned subjectively, they may sample parts of the home range that are favoured or avoided by the habituated group, leading to a mismatch in the densities being estimated by the two approaches. This is exacerbated when the sampled strip(s) extend beyond the home range(s) of the habituated group(s), into other home ranges. Further, lone males are not included in densities obtained from home range studies, so that density might be expected to be lower as assessed by this method than that obtained by appropriate application of line transect sampling methods. In the case of a population of grey-cheeked mangabeys Lophocebus albigena in Uganda (Olupot and Waser, 2005), Olupot (pers. comm.) estimates that around 30% of males are solitary, corresponding to around 8% of the total population.
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Line transect sampling is a common method used in ecology for sampling the sample required. It is an important procedure for estimating the population density of objects in interested study area. There are two main ways to estimate the population density which are parametric and nonparametric estimation methods. In this paper, we present kernel method to propose new estimator of the propose population density. Kernel estimation method is used due to avoid the assumption about the shape of the unknown detectable functions. We investigate the performance of the new estimator using simulation study and compared with the existing estimators. Based on the simulation study, the results show that the proposed estimator preforms better than other well - known estimators.
We begin with a quick description of distance sampling and in particular point transect sampling in the presence of measurement errors. This is followed by presenting the DECAF-TEA main objectives and the data collection and analysis setting. Then a description of the geometry involved follows, together with insights about how one can estimate the position of a sound source detected by two 3D bearing sensors measured with error. Then we describe how errors in 3D bearings might be conceptualized and then simulated. Finally, we conclude with some exploratory simulations presenting the propagation of measurement error in 3D bearings to 3D location estimates, from these to 2D distances projected on the ocean’s bottom, and from those all the way through to final density estimates.
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The classical kernel estimator in line transect sampling as shown in equation (2) provides underestimated values in some cases, and produces estimates with large negative bias (see ). In this paper, we propose a new kernel estimator based on power transformation that can be applied when the shoulder condition is violated. It should be noted that the
Buckland, S.T., R.W. Summers, D.L. Borchers and L. Thomas. 2006. Point transect sampling with traps or lures. Journal of Applied Ecology 43: 377-384. Dawson, D. K. & Efford, M. G. 2009. Bird population density estimated from acoustic signals. Journal of Applied Ecology. 46, 1201-1209 DiTraglia, F.J. 2007. Models of Random Wildlife Movement with an Application to Distance Sampling. MMaths Statistics, University of St Andrews Efford MG, Dawson DK, Borchers DL 2009 Population density estimated from locations of individuals on a passive detector array. Ecology 90:2676-2682
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Conceptually, line transect sampling is a snapshot method  (pp. 31), that is, we consider that the animals are at a fixed location while the survey takes place. Thus, we have probabilistic en- counters between a moving observer and immobile animals . There are models [8, 9] for en- counters between mobile animal populations and observers, but these are limited in use: they rely upon quantities that are difficult to determine (mean animal speed, encounter radius) and they as- sume an ideal free gas movement model which can be unrealistic . Making the third assump- tion avoids such problems. This assumption can be violated in two ways: animals can move in response to the observer, or move independently of the observer. Responsive movement is a com- mon problem in distance sampling surveys and the bias it causes can be reduced by modelling the movement [11, 12] or using double-observer methods . In a few specific cases, independent movement has been modelled (for fish: ; for seabirds: ) to reduce bias; however, these methods are ad hoc and specific to their application. We do not explore responsive movement here; instead, we consider how animal movement independent of the observer affects bias. For whale surveys, it was concluded that such movement may seriously bias the estimate of mean den- sity if the speed of the whales approaches that of the observer, if the encounter region is long rather than wide, or if the probability of sighting a whale is not strongly dependent on the time it spends within that region . Subsequently, simulation revealed that bias was negligible if mean animal speed was one quarter of that of the observer, but not if animal speed was one half that of the ob- server . This conclusion has now been adopted within distance sampling  (pp. 131, 173) and is used to determine if independent animal movement is a problem in particular studies . This is despite these results being pertinent only to the single simulation scenario considered.
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As noted above, distance sampling (in common with other plot sampling methods) is a ‘snapshot’ method, and is therefore biased in the presence of bird movement, because surveying cannot be done instantaneously. This can be especially problematic for point transect sampling, in which the observer stays at a single location for several minutes. Bibby et al. (2000:97) recommend five-minute counts in temperate regions, while Lee and Marsden (2008) suggest count periods ranging from four to 10 minutes dependent on the type of forest bird surveyed. In many surveys, longer counts are conducted, perhaps up to 20 minutes, to enhance detectability. This may be necessary for example for songbird species, if the gap between songbursts often exceeds five minutes, to ensure certain or near-certain detection of birds at the point. However, for species that typically move around within their territories during the period of a count, two problems arise, both of which cause overestimation of density. First, some birds not initially present in the plot move into it during the recording period, and second, birds within the plot are more likely to be detected when close to the point than when further away, leading to too many short distances (see earlier). Both of these problems may be circumvented by using a snapshot approach (Buckland et al. 2001:173). This involves defining a moment (say two minutes after arrival at the point), at which a snapshot of distances from the point is recorded. The observer uses the time before the snapshot moment to detect and locate birds and, as far as possible, keep track of any movements. The positions of those birds are then recorded at the snapshot moment. The observer uses as much time as is needed to confirm locations of birds after the snapshot moment. He or she may also move away from the point at this time, to help triangulate for aural detections. In practice, the presence and location of many individual birds is inferred, rather than being known, at the snapshot moment. For example, a bird which cannot be seen but which sings at the same location both before and after the snapshot moment is inferred to have been there at that moment. Birds whose location is unknown, and cannot be reliably inferred, at the snapshot moment are not recorded; that is, they are treated the same as undetected birds. Provided all birds at or very near the point can be located, this does not bias the method: the ‘detection function’ under this approach is actually the probability that a bird can be located at the snapshot moment as a function of its distance from the point, rather than its probability of detection. The method was successfully tested on four species by Buckland (2006), giving on average lower estimated bias and higher precision than five-minute counts.
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Overall, several limitations were found which might affect the outcome of the experiment. First, the used of different camera lens and setting during the video survey will influence the area cover of the survey area. How- ever, this influence was not taking into consideration within this study. Second, the number of frames extracted was differed although the video recording was taken at a constant camera distance from the substrate. This hap- pened because it was difficult to precisely maintain the camera perpendicularly along the transect line even though a reference bar was used to aid in maintaining the distance of camera from the substrate during video re- cording. Moreover, recording was done by following the reef floor contour rather than the shape of the corals. Consequently, possibility to shoot a video closer to the coral might happen. Third, long hours were needed to analyze the data. Therefore, in order to run the analysis by CPCe software, it would be much preferred to use a computer with high capabilities to withstand huge amount of data as it might reduce time taken during data analysis. Lastly, the CVT technique conducted in this study only survey a horizontal reef floor, thus it is rec- ommended to attempt the CVT technique for a vertical reef floor to prove the ability of CVT technique to con- duct coral survey at various reef conditions.
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We conducted an annual line transect distance sampling exercise from 2002–2018 as part of the third-year applied ecology course at Massey University. The exercise was always conducted over a 2 h period at approximately midday on a day in late July when the weather was expected to be fine. Each of the 11 transects were surveyed by 2–3 people who were instructed to walk at a slow steady pace scanning either side of the transect looking for birds while also taking care to check directly overhead. When a bird was seen, its perpendicular distance from the transect was recorded accurately (to the nearest 0.1 m) with a tape measure, and its species identified using a simple visual guide. Only birds that were seen were recorded. If birds seen in groups (i.e. clusters) were judged to be following one another, the numbers of birds in the cluster was recorded. Birds not following one another were recorded separately. Different species were assumed to be independent of each other. When the transect was finished, the group walked back without recording any birds seen. The same instructions were given each year, so the level of effort applied per length of transect was fairly constant.
concentration is expected (Varljen et al., 2006). Whether purge times (and hence purged volumes) are adequate to attain a steady-state FWA sample concentration, however, is a key issue (Martin-Hayden, 2000; Martin-Hayden et al., 2014; McMillan et al., 2014). The variation in delayed arrival times within the sample taken of groundwater entering the monitoring well screen at distance from the pump intake becomes of critical signiﬁcance here (Martin-Hayden et al., 2014). Where pro- tocols are based upon a speciﬁed number of (standing water) well or screen volumes purged prior to sampling, then greater volumes increase the likelihood of a steady-state FWA sample being obtained. For low- ﬂow (low-stress) sampling protocols, where steady-state conditions are evidenced by stabilization of the pumping water level and water quality indicator parameters (EC, pH, etc.) (Puls and Barcelona, 1996; US EPA, 2010, 2017), these protocols typically do not indicate a minimum purged volume (other than to specify it exceeds any well drawdown measured and sample equipment volume). Where comparatively low volumes are purged, then a partial (rather than full) purge of the ori- ginal standing well (or screen) water volume will occur (Martin-Hayden et al., 2014) and samples may then be expected to variously approach, but not fully constitute, a FWA sample concentration. It is clear from the above that understanding the inﬂuence of employed protocols upon monitoring well assessment of heterogeneous DNAPL source zone areas is of critical importance.
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Abstract. The Northeast China Transect (NECT) is one of the International Geosphere-Biosphere Program (IGBP) ter- restrial transects, where there is a significant precipitation gradient from east to west, as well as a vegetation transition of forest–grassland–desert. It is remarkable to understand vegetation distribution and dynamics under climate change in this transect. We take canopy cover (M), derived from Nor- malized Difference Vegetation Index (NDVI), as an index to describe the properties of vegetation distribution and dynam- ics in the NECT. In Eagleson’s ecohydrological optimality theory, the optimal canopy cover (M ∗ ) is determined by the trade-off between water supply depending on water balance and water demand depending on canopy transpiration. We apply Eagleson’s ecohydrological optimality method in the NECT based on data from 2000 to 2013 to get M ∗ , which is compared with M from NDVI to further discuss the sen- sitivity of M ∗ to vegetation properties and climate factors. The result indicates that the average M ∗ fits the actual M well (for forest, M ∗ = 0.822 while M = 0.826; for grassland, M ∗ = 0.353 while M = 0.352; the correlation coefficient be- tween M and M ∗ is 0.81). Results of water balance also match the field-measured data in the references. The sensi- tivity analyses show that M ∗ decreases with the increase of leaf area index (LAI), stem fraction and temperature, while it increases with the increase of leaf angle and precipita- tion amount. Eagleson’s ecohydrological optimality method offers a quantitative way to understand the impacts of cli- mate change on canopy cover and provides guidelines for ecorestoration projects.
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The goal of the Great Bay work is to continue monitoring an established SeagrassNet site in Great Bay, NH. (This site was established in 2007 with funds from a private philanthropy and the Great Bay National Estuarine Research Reserve.) The site is located in Great Bay with its three cross-transects distributed between Lubberland Creek to the central bay, each running in a north-south direction (see Figure 2). The site has been monitored three to four times per year from 2007 to the present. The monitoring has provided scientific evidence of the eelgrass decline in the Great Bay (Short et al. 2017). This site will be monitored three times: in April, July and October of 2019. More detail on procedures and specific parameters can be found in Section B1: Sampling Process Design.
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degree of reproductive isolation  and by restricted genomic areas of genetic differentiation ("speciation islands" sensu Turner, ), suggesting that the two forms are undergoing a speciation process. From the ecological point of view the S-form seems to be mostly associated with small rain-dependent breeding sites, while the M- form seems more associated with semi-permanent breed- ing sites, frequently created by human activities, such as rice cultivations [9-13]. From the epidemiological point of view, although preliminary observations do not clearly support any difference in the ability of the two forms as malaria vectors [14,15], their different larval ecology may affect their temporal and spatial dynamics and, conse- quently, malaria transmission in some regions, as sug- gested by Touré and collaborators [16,17]. Moreover, the different spread of insecticide resistance mechanisms in the two forms, such as the knock-down resistance (kdr) , should be taken into consideration when planning insecticide-based control activities against these vectors. So far, the only information available on An. gambiae molecular forms along the Gambia River refer to small samples from The Gambia  and Eastern Senegal . This article presents the results of adult An. gambiae s.l. collections along a 400 km west to east transect from the western coastal region of The Gambia (Kartung, 16°45'W) to south-eastern Senegal (Kedougou, 12°07'W), during the 2005 end of rainy season/early dry season and the 2006 rainy season, to study the geographic and temporal distribution of the two molecular forms of An. gambiae s.s. and of the other taxa of the An. gambiae complex sympatric in the area. The results highlight evi- dence of strong bionomic divergence between the M and S-forms and, unexpectedly, reveal that at the extreme west of their range of distribution they show a level of hybrid- ization higher than that reported from other west African geographical areas.
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To obtain abundance estimates of a population of interest using distance sampling methods, lines or points may be placed in the study area according to some design (see Buckland et al., 2001, for details). Each line or point is surveyed at least once following the distance sampling proto- col where the observer travels along the line (line transects) or remains at the point for a fixed amount of time (point transects). Detections are recorded along with the perpendicular distance from the line to the detection or radial distance from the point to the detection. These distances may be recorded exactly or in predetermined distance intervals. Thus, surveys of this type produce two types of data: firstly, the observed distances y e with e = 1, 2, 3, ..., n (n being the total num-
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al., 2015, 2016), orogenic-type Au deposits (Zeng et al., 2013; Liu et al., 2017), and diamondiferous kimberlite occurrences (e.g., McInnes et al., 2009; Evans et al., 2013). McInnes et al. (2005a, b) performed apatite (U-Th)/He, zircon (U-Th)/He, and zircon U-Pb dating on samples from porphyry deposits in Iran, Chile, and Indonesia. Combined with inverse numeri- cal modeling (i.e., 4DTherm; Fu et al., 2010), the data were used to decode thermal histories (magmatic through hydro- thermal and exhumation) over a 700°C temperature interval. However, modeling requires knowledge and assumptions of physicochemical parameters of the igneous bodies and their country rocks (e.g., geothermal gradient, size of intrusion, temperature, etc.), which are not always available. The verti- cal transect method, devised by Wagner and Reimer (1972), has been utilized to deduce simple age-elevation relationships for thermochronometers. This relationship provides a direct basis to estimate long-term erosion rate, independent of geo- thermal gradient and other parameters, and has been used extensively in the study of geomorphic evolution (e.g., Rein- ers et al., 2003; Flowers and Farley, 2012).
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The census was carried out under suitable climat- ic conditions, or during slight weather changes (e.g. a mild short-term rain or drizzle). In the case of heavy rain, the census was stopped and continued only after the rain was over. The year 2010 was ex- treme, it started raining on 1 May and the rain per- sisted almost continuously till the end of the first ten-day period of June. Under such circumstances, it was difficult to finish the census, therefore e.g. the last point at the Vrapač transect was censused in mid-May as late as at 00:30 p.m. The birds re- corded at the monitoring points were determined both visually and acoustically (song, different types of contact calls, alarm calls, attraction calls etc.). During the census in mid-April, the determination of species and individuals was 70–80% acoustic, while in mid-May when most of the trees had al- ready come into leaf, the share of acoustic registra- tions increased to 90–100%. In mid-June, when the
Several transects were collected under a range of different wind conditions with the same gun configuration as used in the determination of the radial leg. These transects were collected using a closer catch-can spacing (1.667m) than that used to collect the zero wind data. Two wind affected transects were chosen from this data with moderate wind speeds and different wind directions: in transect 1 the wind is approximately parallel to the travel direction at 3.97 m/s and 344º while for transect 2 it is nearly perpendicular at 2.52 m/s at 84º. Nozzle sector angles were 284º for both transects.
Overall, the gross percentage of general woody vegetation on farms emerged as the dominant driver of detectability, while vegetation composition (native or introduced) had little effect. As vegetation patches on the surveyed farms varied from coniferous shelterbelts to native bush, this suggests that bird detectability is similar in different woody vegetation types, even though woody vegetation generally obscures birds from detection, and the detectability of birds in vegetation tends to be noticeably different from that in open habitats. Had we simply used bird counts without accounting for detectability, or not built measures of habitat into the Distance analysis to enable variability in detection functions, the true extent of the importance of woody vegetation for biodiversity (Blackwell et al. 2008; Meadows et al. 2008; Moller et al. 2008) would have been obscured. The bias when comparing bird abundance (and perhaps also diversity) in landscapes with differing degrees of woody vegetation patches and open areas will mislead inferences. Therefore we caution that abundance estimation by relative indices (e.g. five-minute bird counts) will only be robust within large and uniformly wooded patches, or within large uniformly open ground. Seasonal differences did not show up as drivers of ESW differences, but season was a significant distance sampling covariate for skylarks in particular. Small sample sizes may have greatly reduced power to detect such effects in other species; it would therefore be unwise to ignore season as affecting detectability when choosing bird monitoring methods.
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The accuracy of the modified line-intercept estimate of plant bug density depends on the variability of the stand, past spray history and efficacy, and most importantly, the skill of the observer. The precision of the line-intercept method depends on the length and number of transect lines used to collect the data and the correct placement of sample lines in homogenous strata ( management units) of the field. When incorporated with line- intercept concepts, the drop cloth is the key tool that permits the scout to examine large size sample units for nymphs and teneral adults with greater efficiency and without excessive costs in time. It is probable that the sweep net also can be used with imagery, but the targeted life stage will be mature adults that exhibit an ability to easily take flight during any disturbance of the plant canopy. At times, all these tools could be employed to best determine plant bug abundance in large cotton fields.
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a standard deviation, calculated from the 1000 runs unique to each transect length–location combination. We used linear and non-linear mixed-effects models to evaluate the influence of transect length and total DWD volume (pre- dictor variables, unique to each location) on the coefficient of variation (CV) of these estimates. Total DWD volume refers to that calculated from the full 340-m transect length for each location. We first tested the predictors in individ- ual models and then combined them to determine the best-fit two-term model. Forest type was included as a random factor in all models, given that we are not making inferences about particular forest types, for which our sam- ple size (3–7 locations per type) is inadequate. Logarithmic and square-root transformations of response and pre- dictor variables were explored for stabilizing variance and improving residual-versus-predicted diagnostics. The fact that our candidate models included trans- formed and non-transformed variables limited our reliance on Akaike’s information criterion (Burnham and Anderson 2002) for evaluating model performance. In- stead, we relied on pseudo R 2 (see below) and root mean square error (RMSE, back transformed as needed) values, as well as graphs of observed-versus-predicted and residual-versus-predicted values to determine which models were best supported by the data. For the one-term models, we tested linear, negative exponential, and power function model forms; for the two-term model, we tested linear, negative exponential, power, quadratic, and partial quadratic forms. Analyses were conducted in the nlme package (Pinheiro et al., 2016) in R (version 3.0.3; Core Team, 2016), using the weighting option to
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