Extinction Rate Estimates for Plant Populations in Revisitation Studies: Importance of Detectability

Abstract:  Many researchers have obtained extinction-rate estimates for plant populations by comparing historical and current records of occurrence. A population that is no longer found is assumed to have gone extinct. Extinction can then be related to characteristics of these populations, such as habitat type, size, or species, to test ideas about what factors may affect extinction. Such studies neglect the fact that a population may be overlooked, however, which may bias estimates of extinction rates upward. In addition, if populations are unequally detectable across groups to be compared, such as habitat type or population size, comparisons become distorted to an unknown degree. To illustrate the problem, I simulated two data sets, assuming a constant extinction rate, in which populations occurred in different habitats or habitats of different size and these factors affected their detectability. The conventional analysis implicitly assumed that detectability equalled 1 and used logistic regression to estimate extinction rates. It wrongly identified habitat and population size as factors affecting extinction risk. In contrast, with capture-recapture methods, unbiased estimates of extinction rates were recovered. I argue that capture-recapture methods should be considered more often in estimations of demographic parameters in plant populations and communities.     

Paving the Way for Invasive Species: Road Type and the Spread of Common Ragweed (<Emphasis Type="Italic">Ambrosia artemisiifolia</Emphasis>)

Roads function as prime habitats and corridors for invasive plant species. Yet despite the diversity of road types, there is little research on the influence of these types on the spread of invaders. Common ragweed (Ambrosia artemisiifolia), a plant producing large amounts of allergenic pollen, was selected as a species model for examining the impact of road type on the spread of invasive plants. We examined this relationship in an agricultural region of Quebec, Canada. We mapped plant distribution along different road types, and constructed a model of species presence. Common ragweed was found in almost all sampling sites located along regional (97%) and local paved (81%) roads. However, verges of unpaved local roads were rarely (13%) colonized by the plant. A model (53% of variance explained), constructed with only four variables (paved regional roads, paved local roads, recently mown road verges, forest cover), correctly predicted (success rate: 89%) the spatial distribution of common ragweed. Results support the hypothesis that attributes associated with paved roads strongly favour the spread of an opportunistic invasive plant species. Specifically, larger verges and greater disturbance associated with higher traffic volume create propitious conditions for common ragweed. To date, emphasis has been placed on controlling the plant in agricultural fields, even though roadsides are probably a much larger seed source. Strategies for controlling the weed along roads have only focused on major highways, even though the considerable populations along local roads also contribute to the production of pollen. Management prioritizations developed to control common ragweed are thus questionable.     

Use of Multispecies Occupancy Models to Evaluate the Response of Bird Communities to Forest Degradation Associated with Logging

Forest degradation is arguably the greatest threat to biodiversity, ecosystem services, and rural livelihoods. Therefore, increasing understanding of how organisms respond to degradation is essential for management and conservation planning. We were motivated by the need for rapid and practical analytical tools to assess the influence of management and degradation on biodiversity and system state in areas subject to rapid environmental change. We compared bird community composition and size in managed (ejido, i.e., communally owned lands) and unmanaged (national park) forests in the Sierra Tarahumara region, Mexico, using multispecies occupancy models and data from a 2‐year breeding bird survey. Unmanaged sites had on average higher species occupancy and richness than managed sites. Most species were present in low numbers as indicated by lower values of detection and occupancy associated with logging‐induced degradation. Less than 10% of species had occupancy probabilities >0.5, and degradation had no positive effects on occupancy. The estimated metacommunity size of 125 exceeded previous estimates for the region, and sites with mature trees and uneven‐aged forest stand characteristics contained the highest species richness. Higher estimation uncertainty and decreases in richness and occupancy for all species, including habitat generalists, were associated with degraded young, even‐aged stands. Our findings show that multispecies occupancy methods provide tractable measures of biodiversity and system state and valuable decision support for landholders and managers. These techniques can be used to rapidly address gaps in biodiversity information, threats to biodiversity, and vulnerabilities of species of interest on a landscape level, even in degraded or fast‐changing environments. Moreover, such tools may be particularly relevant in the assessment of species richness and distribution in a wide array of habitats. Uso de Modelos de Ocupación para Múltiples Especies para Evaluar la Respuesta de las Comunidades de Aves a la Degradación de Bosques Asociada con la Tala     

Use of Spatial Capture‐Recapture Modeling and DNA Data to Estimate Densities of Elusive Animals

Abstract: Assessment of abundance, survival, recruitment rates, and density (i.e., population assessment) is especially challenging for elusive species most in need of protection (e.g., rare carnivores). Individual identification methods, such as DNA sampling, provide ways of studying such species efficiently and noninvasively. Additionally, statistical methods that correct for undetected animals and account for locations where animals are captured are available to efficiently estimate density and other demographic parameters. We collected hair samples of European wildcat (Felis silvestris) from cheek‐rub lure sticks, extracted DNA from the samples, and identified each animals’ genotype. To estimate the density of wildcats, we used Bayesian inference in a spatial capture‐recapture model. We used WinBUGS to fit a model that accounted for differences in detection probability among individuals and seasons and between two lure arrays. We detected 21 individual wildcats (including possible hybrids) 47 times. Wildcat density was estimated at 0.29/km² (SE 0.06), and 95% of the activity of wildcats was estimated to occur within 1.83 km from their home‐range center. Lures located systematically were associated with a greater number of detections than lures placed in a cell on the basis of expert opinion. Detection probability of individual cats was greatest in late March. Our model is a generalized linear mixed model; hence, it can be easily extended, for instance, to incorporate trap‐ and individual‐level covariates. We believe that the combined use of noninvasive sampling techniques and spatial capture‐recapture models will improve population assessments, especially for rare and elusive animals.     

Evidence of an Edge Effect on Avian Nest Success

Abstract:  Habitat fragmentation may modify ecological patterns by increasing the importance of edge effects, including elevating rates of predation on avian nests. Conventional wisdom suggests an increased rate of predation along habitat edges, and previous reviews support this view. These reviews did not apply recent statistical approaches, however, and some were based on a small number of studies. In our meta-analysis of 64 nest-predation experiments, our results supported prior reviews of the general pattern of increased nest predation along habitat edges (  p < 0.01). We separated studies into ecologically relevant categories and found the following patterns: (1) Edge effects were more pronounced in North America and northwestern Europe than in central Europe or Central America. This result may be biased, however, by the different habitats studied in the regions. (2) Marshes and deciduous forests had significant edge effects, whereas edge effects were not apparent in coniferous forests, tropical forests, or fields. (3) Ground and natural nest studies were more likely to exhibit edge effects. (4) Edge effects were detected in studies that used quail eggs and real eggs. (5) Edge effects were not significant when artificial nests were exposed for typical incubation periods, but were significant for shorter exposures. Three alternative hypotheses may explain increased nest predation along edges. The edge-effects hypothesis states that increased nest losses along edges are the result of the habitat discontinuity. The landscape-structure hypothesis states that more fragmented landscapes are more heavily depredated by nest predators. The human-disturbance hypothesis states that near anthropogenic edges increased nest predation is related to human activities. Nest-predation experiments should be placed in a landscape context to reveal differences between the hypotheses.     

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.