Presence–Absence versus Abundance Data for Monitoring Threatened Species

Abstract:  Effective detection of population trend is crucial for managing threatened species. Little theory exists, however, to assist managers in choosing the most cost-effective monitoring techniques for diagnosing trend. We present a framework for determining the optimal monitoring strategy by simulating a manager collecting data on a declining species, the Chestnut-rumped Hylacola ( Hylacola pyrrhopygia parkeri ), to determine whether the species should be listed under the IUCN (World Conservation Union) Red List. We compared the efficiencies of two strategies for detecting trend, abundance, and presence–absence surveys, under financial constraints. One might expect the abundance surveys to be superior under all circumstances because more information is collected at each site. Nevertheless, the presence–absence data can be collected at more sites because the surveyor is not obliged to spend a fixed amount of time at each site. The optimal strategy for monitoring was very dependent on the budget available. Under some circumstances, presence–absence surveys outperformed abundance surveys for diagnosing the IUCN Red List categories cost-effectively. Abundance surveys were best if the species was expected to be recorded more than 16 times/year; otherwise, presence–absence surveys were best. The relationship between the strategies we investigated is likely to be relevant for many comparisons of presence–absence or abundance data. Managers of any cryptic or low-density species who hope to maximize their success of estimating trend should find an application for our results.     

Importance of Well-Designed Monitoring Programs for the Conservation of Endangered Species: Case Study of the Snail Kite

Abstract:  Monitoring natural populations is often a necessary step to establish the conservation status of species and to help improve management decisions. Nevertheless, many monitoring programs do not effectively address primary sources of variability in monitoring data, which ultimately may limit the utility of monitoring in identifying declines and improving management. To illustrate the importance of taking into account detectability and spatial variation, we used a recently proposed estimator of abundance (superpopulation estimator) to estimate population size of and number of young produced by the Snail Kite ( Rostrhamus sociabilis plumbeus ) in Florida. During the last decade, primary recovery targets set by the U.S. Fish and Wildlife Service for the Snail Kite that were based on deficient monitoring programs (i.e., uncorrected counts) were close to being met (by simply increasing search effort during count surveys). During that same period, the Snail Kite population declined dramatically (by 55% from 1997 to 2005) and the number of young decreased by 70% between 1992–1998 and 1999–2005. Our results provide a strong practical case in favor of the argument that investing a sufficient amount of time and resources into designing and implementing monitoring programs that carefully address detectability and spatial variation is critical for the conservation of endangered species.     

Site‐Occupancy Distribution Modeling to Correct Population‐Trend Estimates Derived from Opportunistic Observations

Abstract: Species’ assessments must frequently be derived from opportunistic observations made by volunteers (i.e., citizen scientists). Interpretation of the resulting data to estimate population trends is plagued with problems, including teasing apart genuine population trends from variations in observation effort. We devised a way to correct for annual variation in effort when estimating trends in occupancy (species distribution) from faunal or floral databases of opportunistic observations. First, for all surveyed sites, detection histories (i.e., strings of detection–nondetection records) are generated. Within‐season replicate surveys provide information on the detectability of an occupied site. Detectability directly represents observation effort; hence, estimating detectablity means correcting for observation effort. Second, site‐occupancy models are applied directly to the detection‐history data set (i.e., without aggregation by site and year) to estimate detectability and species distribution (occupancy, i.e., the true proportion of sites where a species occurs). Site‐occupancy models also provide unbiased estimators of components of distributional change (i.e., colonization and extinction rates). We illustrate our method with data from a large citizen‐science project in Switzerland in which field ornithologists record opportunistic observations. We analyzed data collected on four species: the widespread Kingfisher (Alcedo atthis) and Sparrowhawk (Accipiter nisus) and the scarce Rock Thrush (Monticola saxatilis) and Wallcreeper (Tichodroma muraria). Our method requires that all observed species are recorded. Detectability was <1 and varied over the years. Simulations suggested some robustness, but we advocate recording complete species lists (checklists), rather than recording individual records of single species. The representation of observation effort with its effect on detectability provides a solution to the problem of differences in effort encountered when extracting trend information from haphazard observations. We expect our method is widely applicable for global biodiversity monitoring and modeling of species distributions.     

Detecting Population Declines over Large Areas with Presence-Absence, Time-to-Encounter, and Count Survey Methods

Abstract:  Ecologists often discount presence-absence surveys as a poor way to gain insight into population dynamics, in part because these surveys are not amenable to many standard statistical tests. Still, presence-absence surveys are sometimes the only feasible alternative for monitoring large areas when funds are limited, especially for sparse or difficult-to-detect species. I undertook a detailed simulation study to compare the power of presence-absence, count, and time-to-encounter surveys to detect regional declines in a population. I used a modeling approach that simulates both population numbers and the monitoring process, accounting for observation and other measurement errors. In gauging the efficacy of presence-absence surveys versus other approaches, I varied the number of survey sites, the spatial variation in encounter rate, the mean encounter rate, and the type of population loss. My results showed that presence-absence data can be as or more powerful than count data in many cases. Quantitative guidelines for choosing between presence-absence surveys and count surveys depend on the biological and logistical constraints governing a conservation monitoring situation. Generally, presence-absence surveys work best when there is little variability in abundance among the survey sites, the organism is rare, and the species is difficult to detect so that the time spent getting to each survey site is less than or equal to the time spent surveying each site. Count surveys work best otherwise. I present a case study with count data on the Northern Flicker ( Colaptes auratus ) from the North American Breeding Bird Survey to illustrate how the method might be used with field-survey data. The case study demonstrates that a count survey would be the most cost-effective design but would entail reduction in the number of sites. If this site reduction is not desirable, a presence-absence survey would be the most cost-effective survey.     

Effects of Detectability on Estimates of Geographic Range Size in Bignonieae

Abstract: Extinction risk has not been evaluated for 96% of all described plant species. Given that the Global Strategy for Plant Conservation proposes preliminary conservation assessments of all described plant species by 2010, herbarium specimens (i.e., primary occurrence data) are increasingly being used to infer threat components from estimates of geographic range size. Nevertheless, estimates of range size based on herbarium data may be inaccurate due to collection bias associated with interspecific variation in detectability. We used data on 377 species of Bignonieae to test the hypothesis that there is a positive relationship between detectability and estimates of geographic range size derived from herbarium specimens. This relationship is expected if the proportion of the true geographic range size of a species that is documented by herbarium specimens is given by the product of the true geographic range size and the detectability of the species, assuming no relationship between true geographic range size and detectability. We developed 4 measures of detectability that can be estimated from herbarium data and examined the relationship between detectability and 2 types of estimates of geographic range size: area of occupancy and extent of occurrence. Our results from regressing estimates of extent of occurrence and area of occupancy on detectability across genera provided no support for this hypothesis. The same was true for regressions of estimated extent of occurrence on detectability across species within genera. Nevertheless, regressions of estimated area of occupancy on detectability across species within genera provided partial support for our hypothesis. We considered 3 possible explanations for this mixed outcome: violation of the assumption of no relationship between true geographic range size and detectability; the relationships between estimated geographic range size and detectability may be an artifact of a negative relationship between estimated area of occupancy and the sampling variance of detectability; detectability may have had 2 opposite effects on estimated species range sizes: one determines the proportion of the true range of a species documented by herbarium specimens and the other determines the distribution of true range size for the species actually observed with herbarium data. Our findings should help improve understanding of the potential biases incurred with the use of herbarium data.     

Animal abundance estimation in independent observer line transect surveys

The theory of conventional line transect surveys is based on an essential assumption that 100% detection of animals right on the transect lines can be achieved. When this assumption fails, independent observer line transect surveys are used. This paper proposes a general approach, based on a conditional likelihood, which can be carried out either parametrically or nonparametrically, to estimate the abundance of non-clustered biological populations using data collected from independent observer line transect surveys. A nonparametric estimator is specifically proposed which combines the conditional likelihood and the kernel smoothing method. It has the advantage that it allows the data themselves to dictate the form of the detection function, free of any subjective choice. The bias and the variance of the nonparametric estimator are given. Its asymptotic normality is established which enables construction of confidence intervals. A simulation study shows that the proposed estimator has good empirical performance, and the confidence intervals have good coverage accuracy.     

Bridging information domains to improve ecological understanding of biological invasions in a marine ecosystem

We devised a practical method for integrating information on 2 marine invasive species using 3 different approaches: standardized ecological monitoring, online-reporting databases, and surveys of anglers and crabbers. Focusing on 2 recently introduced species with different characteristics, the Asian shore crab (Hemigrapsus sanguineus) and Chinese mitten crab (Eriocheir sinensis), in the Hudson-Raritan watershed of New York and New Jersey, we used sensitivity analyses to explore the relative contribution of each information source to knowledge of species abundance and distribution. All 3 information sources contributed something unique to understanding abundance and distribution of the introduced crabs. Online and survey data on Asian shore crabs significantly affected predictions of abundance, whereas monitoring data did not. When survey data were omitted, abundance estimates were unchanged over time, but when they were included, the model predicted an increased abundance in 2012. All 3 data sets for the Asian shore crab significantly affected estimates of species coverage; surveys had the biggest influence, increasing range size by 4097.25 km². For the catadromous Chinese mitten crab, ecological monitoring data collected in freshwater shortly after the original sighting significantly shaped model estimates for abundance and documented the establishment phase of the mitten crab in an area outside the spatial scope of the surveyed resource users. However, the survey data significantly enlarged mitten crab range-size estimates by 6498.01 km². By demonstrating that data integration produced an image of the invasion process that would not have emerged had we used any 1 method individually, model results provide evidence for the advantages of an interdisciplinary approach.     

Determining soil quality in urban agricultural regions by soil enzyme-based index

Urban agricultural soils are highly variable, and careful selection of sensitive indicators is needed for the assessment of soil quality. This study is proposed to develop an index based on soil enzyme activities for assessing the quality of urban agricultural soils. Top soils were collected from urban agricultural areas of Korea, and soil chemical properties, texture, microbial fatty acids, and enzyme activities were determined. The soils belonged to five textural classes with the highest frequency of sandy loam. There was no clear correlation between the soil chemical properties and soil microbial properties. Principal component analysis (PCA) and factor analysis were applied to microbial groups for identification of microbial community variation in soils. Two soil groups, namely group 1 (G1) and group 2 (G2), based on microbial community abundance were examined by PCA, and those were more prominent in factor analysis. The G1 soils showed higher microbial community abundance than G2 soils. The canonical discriminant analysis was applied to the enzyme activities of sandy loam soil to develop an index, and the index validation was confirmed using the unused soils and published data. The high-quality soils in published literature assigned the high valued index. Microbial fatty acids and soil enzyme activities can be suitable indicators for soil quality evaluation of urban agricultural soils.

     

Spatial variogram estimation from temporally aggregated seabird count data

Seabird abundance is an important indicator for assessing impact of human activities on the marine environment. However, data collection at sea is time consuming and surveys are carried out over several consecutive days for efficiency reasons. This study investigates the validity of aggregating those data over time to estimate a spatial variogram that is representative for spatial correlation in species abundance. For this purpose we simulate four-day surveys of seabird count data that contain spatial and temporal correlation arising from temporal changes in the spatial pattern of environmental conditions. Estimates of the aggregated spatial variogram are compared to a variogram that would arise when data were collected over a single day. The study reveals that, under changing environmental conditions over surveys days, aggregating data over a four-day survey increases both the non-spatial variation in the data and the scale of spatial correlation in seabird data. Next, the effect of using an aggregated variogram on the statistical power to test the significance of an impact is investigated. The impact concerns a case of establishing an offshore wind farm resulting in seabird displacement. The study shows that both overestimation and underestimation of statistical power occurs, with power estimates differing up to a factor of two. We conclude that the spatial variation in seabird abundance can be misrepresented by using temporally aggregated data. In impact studies, such misrepresentation can lead to erroneous assessments of the ability to detect impact.     

Importance of Accounting for Detection Heterogeneity When Estimating Abundance: the Case of French Wolves

Abstract: Assessing conservation strategies requires reliable estimates of abundance. Because detecting all individuals is most often impossible in free‐ranging populations, estimation procedures have to account for a <1 detection probability. Capture–recapture methods allow biologists to cope with this issue of detectability. Nevertheless, capture–recapture models for open populations are built on the assumption that all individuals share the same detection probability, although detection heterogeneity among individuals has led to underestimating abundance of closed populations. We developed multievent capture–recapture models for an open population and proposed an associated estimator of population size that both account for individual detection heterogeneity (IDH). We considered a two‐class mixture model with weakly and highly detectable individuals to account for IDH. In a noninvasive capture–recapture study of wolves we based on genotypes identified in feces and hairs, we found a large underestimation of population size (27% on average) occurred when IDH was ignored.     

Avian predators are less abundant during periodical cicada emergences, but why?

Despite a substantial resource pulse, numerous avian insectivores known to depredate periodical cicadas (Magicicada spp.) are detected less commonly during emergence years than in either the previous or following years. We used data on periodical cicada calls collected by volunteers conducting North American Breeding Bird Surveys within the range of cicada Brood X to test three hypotheses for this observation: lower detection rates could be caused by bird calls being obscured by cicada calls ("detectability" hypothesis), by birds avoiding areas with cicadas ("repel" hypothesis), or because bird abundances are generally lower during emergence years for some reason unrelated to the current emergence event ("true decline" hypothesis). We tested these hypotheses by comparing bird detections at stations coincident with calling cicadas vs. those without calling cicadas in the year prior to and during cicada emergences. At four distinct levels (stop, route, range, and season), parallel declines of birds in groups exposed and not exposed to cicada calls supported the true decline hypothesis. We discuss several potential mechanisms for this pattern, including the possibility that it is a consequence of the ecological and evolutionary interactions between predators of this extraordinary group of insects.     

Simultaneous-count models to estimate abundance from counts of unmarked individuals with imperfect detection

We developed a method to estimate population abundance from simultaneous counts of unmarked individuals over multiple sites. We considered that at each sampling occasion, individuals in a population could be detected at 1 of the survey sites or remain undetected and used either multinomial or binomial simultaneous-count models to estimate abundance, the latter being equivalent to an N-mixture model with one site. We tested model performance with simulations over a range of detection probabilities, population sizes, growth rates, number of years, sampling occasions, and sites. We then applied our method to 3 critically endangered vulture species in Cambodia to demonstrate the real-world applicability of the model and to provide the first abundance estimates for these species in Cambodia. Our new approach works best when existing methods are expected to perform poorly (i.e., few sites and large variation in abundance among sites) and if individuals may move among sites between sampling occasions. The approach performed better when there were >8 sampling occasions and net probability of detection was high (>0.5). We believe our approach will be useful in particular for simultaneous surveys at aggregation sites, such as roosts. The method complements existing approaches for estimating abundance of unmarked individuals and is the first method designed specifically for simultaneous counts.     

Fishery Stability, Local Extinctions, and Shifts in Community Structure in Skates

Abstract: Skates are arguably the most vulnerable of exploited marine fishes. Their vulnerability is often assessed by examining fisheries catch trends, but these data are not generally recorded on a species basis except in France. Aggregated skate catch statistics tend to exhibit more stable trends than those of other elasmobranch fisheries. We tested whether such apparent stability in aggregated catch trends could mask population declines of individual species. We examined two time series of species-specific surveys of a relatively stable skate fishery in the northeast Atlantic. These surveys revealed the disappearance of two skate species, long-nose skate (   Dipturus oxyrhinchus ) and white skate (   Rostroraja alba ) and confirmed a previously documented decline of the common skate (   D. batis ). Of the remaining five skate species, the three larger ones have declined, whereas two smaller species have increased in abundance. The increase in abundance and biomass of the smaller species has resulted in the stability of the aggregated catch trends. Because there is significant dietary overlap among species, we suggest the increase in abundance of the smaller species may be due to competitive release as the larger species declined. A consequence of this kind of stability is that declining species cannot be detected without species-specific data, especially in taxa exhibiting competitive interactions. This may explain why previously documented disappearances of two species of skates went unnoticed for so long. The conservation of skates and other elasmobranchs requires species-specific monitoring and special attention to larger species.     

Spatial assessment of fin whale hotspots and their association with krill within an important Antarctic feeding and fishing ground

Fin whale (Balaenoptera physalus quoyi) habitat use and its relationship to environmental conditions are generally unknown in the Southern Ocean, presenting challenges for predicting their seasonal occurrence and potential effects of fishing pressure and climate change on this endangered species. Using biological data collected during 14 shipboard surveys off the northern Antarctic Peninsula and oceanographic data from satellite remote sensing, we mapped the distribution of fin whale hotspots, Antarctic krill abundance (biomass from acoustics, concentrations from nets) and ocean conditions during mid- and late-summer to investigate the environmental determinants of whale hotspots. Generalized additive models (GAM) were used to test the hypothesis that intra-seasonal changes in fin whale hotspot distribution relate to sea surface temperature (SST), krill abundance and eddy kinetic energy (EKE). More whale hotspots (sightings and individuals) are observed during late- than mid-summer surveys. During mid-summer, hotspots occurred near Elephant Island while in late-summer they were distributed throughout the slope region in proximity to the mean location of the southern Antarctic Circumpolar Current Front. The spatial mean of EKE did not differ between mid- and late-summer surveys, but the spatial mean of SST was significantly warmer during late-summer. The GAM for mid-summer indicates that fin whale hotspots were positively related to SST, EKE and acoustically determined krill biomass. The GAM for late-summer indicates the hotspots were negatively related to net-based krill abundance and positively related to acoustic krill biomass and EKE. This study is important because environmental determinants of fin whale hotspots may be used as reference points for implementing future conservation plans for their recovering populations.     

Understanding abundance patterns of a declining seabird: implications for monitoring.

The Kittlitz's Murrelet (Brachyramphus brevirostris) is a rare, non-colonial seabird often associated with tidewater glaciers and a recent candidate for listing under the Endangered Species Act. We estimated abundance of Kittlitz's Murrelets across space and time from at-sea surveys along the coast of Alaska (USA) and then used these data to develop spatial models to describe abundance patterns and identify environmental factors affecting abundance. Over a five-week period in the summer of 2005, we recorded 794 Kittlitz's Murrelets, 16 Marbled Murrelets (B. marmoratus), and 70 unidentified murrelets. The overall population estimate (N, mean +/- SE) during the peak period (3-9 July) was 1317 +/- 294 birds, decreasing to 68 +/- 37 by the last survey period (31 July-6 August). Density of Kittlitz's Murrelets was highest in pelagic waters of Taan Fjord (18.6 +/- 7.8 birds/km2, mean +/- SE) during 10-16 July. Spatial models identified consistent "hotspots" of Kittlitz's Murrelets, including several small areas where high densities of murrelets were found throughout the survey period. Of the explanatory variables that we evaluated, tidal current strength influenced murrelet abundance most consistently, with higher abundance associated with strong tidal currents. Simulations based on the empirically derived estimates of variation demonstrated that spatial variation strongly influenced power to detect trend, although power changed little across the threefold difference in the coefficient of variation on detection probability. We include recommendations for monitoring Kittlitz's Murrelets (or other marine species) when there is a high degree of uncertainty about factors affecting abundance, especially spatial variability.     

Line transect sampling of Karner blue butterflies ( Lycaeides melissa samuelis)

Line transect sampling is an effective survey method for estimating butterfly densities because it provides unbiased estimates of site-density (provided key assumptions are met), and estimates are comparable among sites. For monitoring Karner blue butterflies in Wisconsin, USA, comparable estimates are required because each year a different selection of sites will be monitored. Annual state-wide indices of species abundance can be derived from the site-surveys and compared to previous year's indices to monitor trends. We advocate that line transect sampling is preferable to Pollard-Yates transects as a survey technique for monitoring Karner blue butter- flies. The Pollard-Yates surveys do not adjust for diferences in site detectability. As a consequence, estimates of among-site from Pollard-Yates surveys can be biased. © Rapid Science 1998     

Characterizing species abundance distributions across taxa and ecosystems using a simple maximum entropy model

The species abundance distribution (SAD) is one of themost studied patterns in ecology due to its potential insights into commonness and rarity, community assembly, and patterns of biodiversity. It is well established that communities are composed of a few common and many rare species, and numerous theoretical models have been proposed to explain this pattern. However, no attempt has been made to determine how well these theoretical characterizations capture observed taxonomic and global-scale spatial variation in the general form of the distribution. Here, using data of a scope unprecedented in community ecology, we show that a simple maximum entropy model produces a truncated log-series distribution that can predict between 83% and 93% of the observed variation in the rank abundance of species across 15 848 globally distributed communities including birds, mammals, plants, and butterflies. This model requires knowledge of only the species richness and total abundance of the community to predict the full abundance distribution, which suggests that these factors are sufficient to understand the distribution for most purposes. Since geographic patterns in richness and abundance can often be successfully modeled, this approach should allow the distribution of commonness and rarity to be characterized, even in locations where empirical data are unavailable.     

Climate Warming and Calling Phenology of Frogs near Ithaca, New York, 1900–1999

Abstract: Because ambient temperature strongly influences reproduction in frogs, the seasonal timing of frog calling provides a sensitive index of biotic response to climate change. Over the last century, daily temperatures increased during 5 of the 8 months key to gametogenesis in frogs and toads near Ithaca, New York ( U.S.A.). Earliest dates of calling frogs recorded by Albert Hazen Wright between 1900 and 1912 near Ithaca were compared to those from the New York State Amphibian and Reptile Atlas Project for 1990–1999 for the three counties surrounding Ithaca. Four species are now calling 10–13 days earlier, two are unchanged, and none is calling later. The data suggest that climate has warmed in central New York State during this century and has resulted in earlier breeding in some amphibians—a possible first indication of biotic response to climate change in eastern North America.     

Estimating species occurrence, abundance, and detection probability using zero-inflated distributions

Researchers have developed methods to account for imperfect detection of species with either occupancy (presence absence) or count data using replicated sampling. We show how these approaches can be combined to simultaneously estimate occurrence, abundance, and detection probability by specifying a zero-inflated distribution for abundance. This approach may be particularly appropriate when patterns of occurrence and abundance arise from distinct processes operating at differing spatial or temporal scales. We apply the model to two data sets: (1) previously published data for a species of duck, Anas platyrhynchos, and (2) data for a stream fish species, Etheostoma scotti. We show that in these cases, an incomplete-detection zero-inflated modeling approach yields a superior fit to the data than other models. We propose that zero-inflated abundance models accounting for incomplete detection be considered when replicate count data are available.     

Enchytraeid population dynamics: Resource limitation and size-dependent mortality

Enchytraeids are regarded as keystone soil organisms in forest ecosystems. Their abundance and biomass fluctuate widely. Predicting the consequences of anthropogenic disturbances requires an understanding of the mechanisms underlying enchytraeid population dynamics. Here I develop a simple model, which predicts that the type of dynamics is controlled by resource input rate. If fungal resource input is a discrete event once a year, an exponential growth phase is followed by starvation and sharp decline of enchytraeid abundance. Model simulations with three different forcing functions were compared to field data. Initial parameter values were obtained from various independent sources, and parameters were estimated by minimizing the residual sum of squares. The best fitting model with resource addition once a year explained 39% of the variation in enchytraeid biomass over an 8-year study period. Further, variation in rainfall explained 59% of the variation in R² of the exponential phase models, which is also an index of the stability of population size-structure. The results emphasize the importance of resource limitation for enchytraeid population dynamics and support the hypothesis that the mortality during the decline phase is size-dependent.