Inferring extinction of mammals from sighting records, threats, and biological traits

For species with five or more sightings, quantitative techniques exist to test whether a species is extinct on the basis of distribution of sightings. However, 70% of purportedly extinct mammals are known from fewer than five sightings, and such models do not include some important indicators of the likelihood of extinction such as threats, biological traits, search effort, and demography. Previously, we developed a quantitative method that we based on species' traits in which we used Cox proportional hazards regression to calculate the probability of rediscovery of species regarded as extinct. Here, we used two versions of the Cox regression model to determine the probability of extinction in purportedly extinct mammals and compared the results of these two models with those of stationary Poisson, nonparametric, and Weibull sighting-distribution models. For mammals with five or more sightings, the stationary Poisson model categorized all but two critically endangered (flagged as possibly extinct) species in our data set as extinct, and results with this model were consistent with current categories of the International Union for the Conservation of Nature. The scores of probability of rediscovery for individual species in one version of our Cox regression model were correlated with scores assigned by the stationary Poisson model. Thus, we used this Cox regression model to determine the probability of extinction of mammals with sparse records. On the basis of the Cox regression model, the most likely mammals to be rediscovered were the Montane monkey-faced bat (Pteralopex pulchra), Armenian myotis (Myotis hajastanicus), Alcorn's pocket gopher (Pappogeomys alcorni), and Wimmer's shrew (Crocidura wimmeri). The Cox model categorized two species that have recently disappeared as extinct: the baiji (Lipotes vexillifer) and the Christmas Island pipistrelle (Pipistrellus murrayi). Our new method can be used to test whether species with few records or recent last-sighting dates are likely to be extinct.     

Decomposing the mid-Holocene Tsuga decline in eastern North America

The mid-Holocene decline of Tsuga canadensis (hereafter Tsuga) populations across eastern North America is widely perceived as a synchronous event, driven by pests/pathogens, rapid climate change, or both. Pattern identification and causal attribution are hampered by low stratigraphic density of pollen-sampling and radiometric dates at most sites, and by absence of highly resolved, paired pollen and paleoclimate records from single sediment cores, where chronological order of climatic and vegetational changes can be assessed. We present an intensely sampled (contiguous 1-cm intervals) record of pollen and water table depth (inferred from testate amoebae) from a single core spanning the Tsuga decline at Irwin Smith Bog in Lower Michigan, with high-precision chronology. We also present an intensively sampled pollen record from Tower Lake in Upper Michigan. Both sites show high-magnitude fluctuations in Tsuga pollen percentages during the pre-decline maximum. The terminal decline is dated at both sites ca. 5000 cal yr BP, some 400 years later than estimates from other sites and data compilations. The terminal Tsuga decline was evidently heterochronous across its range. A transient decline ca. 5350 cal yr BP at both sites may correspond to the terminal decline at other sites in eastern North America. At Irwin Smith Bog, the terminal Tsuga decline preceded an abrupt and persistent decline in water table depths by approximately 200 years, suggesting the decline was not directly driven by abrupt climate change. The Tsuga decline may best be viewed as comprising at least three phases: a long-duration pre-decline maximum with high-magnitude and high-frequency fluctuations, followed by a terminal decline at individual sites, followed in turn by two millennia of persistently low Tsuga populations. These phases may not be causally linked, and may represent dynamics taking place at multiple temporal and spatial scales. Further progress toward understanding the phenomenon requires an expanded network of high-resolution pollen and paleoclimate chronologies.     

Environmentally Friendly Disposal Behaviour and Local Support Systems: Lessons from a metropolitan area

This paper describes some of the insights gained by the authors in the development of an approach for systemic sustainability analysis to arrive at sustainability indicators (SIs) for development. The paper describes the problems of perspective and mindset which such research involves, and the necessity to rethink both the purpose and content of SIs as well as taking into account the perspective of the researcher. The result represents a new perspective on the classification of SIs that serves to highlight one of the central difficulties encountered so far with these tools, namely their limited use in management and the setting of policy. We argue that this is due in large part to the nature of the SI frameworks created to date, even if carried out in a 'participative' mode. The framework itself is representative of a mindset or paradigm of understanding which, when applied as the sole device, we find less than adequate in achieving useful SIs. SIs arising from this mindset tend to be quantitative and explicit (clearly stated and with a defined methodology), while in practice most people's and institutions' use of SIs tends to be more qualitative and implicit ('understood' to apply in vaguer terms, with no defined methodology). These two paradigms or mindsets are represented here as the reductionist and the conversational : the first is characterised by quantitative and explicit indicators (or QNE* indicators); and the second is characterised by qualitative and implicit indicators (QLI* indicators). We suggest that what is required is far more research on the evolution and use of QLI* SIs (and the mindset which is necessary to develop them), in order to best appreciate how they can be hybridised with the QNE* group. The result may be termed 'multiple perspective' SIs. Este documento describe algunas de las ideas obtenidas por los autores en el desarrollo de un acercamiento por análisis de sostenimiento sistemático para llegar a los indicadores de sostenimiento (SIs) por desarrollo. El documento describe los problemas de perspectiva y patrones que dicha investigación involucra, y la necesidad de repensar tanto en el propósito como en el contenido de los SIs así como tambien toma en cuenta la perspectiva del investigador. El resultado representa una nueva perspectiva en la clasificación del los SIs la cual sirve para resaltar una de las dificultades centrales encontradas hasta ahora con estas herramientas; especificamente su limitado uso en el manejo y establecimiento de políticas. Nosotros argumentamos que ésto se debe en gran parte a la naturaleza de las estructuras de los SIs creadas hasta la fecha, incluso si se lleva a cabo en un modo 'participativo'. La estructura por si misma es representativa de un patron o paradigma de entendimiento el cual cuando es aplicado al esquema único encontramos menos adecuado alcanzar SIs útiles. Los SIs que surgen de este patrón tienden a ser cuantitativos y explícitos (claramente señalados y con una metodología definida), mientras que en la practica el uso de los SIs por parte de la mayoria de la personas e instituciones tiende a ser mas cualitativo e implicito ('entendido' a aplicar en terminos mas vagos, con metodología mas definida). Estos dos paradigmas o patrones son representados aquí como el reduccionista y el coloquial — uno es caracterizado por indicadores cuantitativos y explícitos (o indicadores QNE*), el otro por indicadores cualitativos e implícitos (QLI*) respectivamente. Nosotros sugerimos que lo que se requiere es mucha mas investigación en la evolución y uso de SIs de QLI* (y el patrón que es necesario para desarollarlo), para apreciar mejor como ellos pueden ser mezclados con el grupo de QNE*. El resultado puede ser denominado indicadores de sostenimiento de perspectiva multiple.     

A minimum data set of water quality parameters to assess and compare treatment efficiency of stormwater facilities

Urban stormwater runoff is often of poor quality, impacting aquatic ecosystems and limiting the use of stormwater runoff for recreational purposes. Several stormwater treatment facilities (STFs) are in operation or at the pilot testing stage, but their efficiencies are neither well documented nor easily compared due to the complex contaminant profile of stormwater and the highly variable runoff hydrograph. On the basis of a review of available data sets on urban stormwater quality and environmental contaminant behavior, we suggest a few carefully selected contaminant parameters (the minimum data set) to be obligatory when assessing and comparing the efficiency of STFs. Consistent use of the minimum data set in all future monitoring schemes for STFs will ensure broad-spectrum testing at low costs and strengthen comparability among facilities. The proposed minimum data set includes: (i) fine fraction of suspended solids (<63 μm), (ii) total concentrations of zinc and copper, (iii) total concentrations of phenanthrene, fluoranthene, and benzo(b,k)fluoranthene, and (iv) total concentrations of phosphorus and nitrogen. Indicator pathogens and other specific contaminants (i.e., chromium, pesticides, phenols) may be added if recreational or certain catchment-scale objectives are to be met. Issues that need further investigation have been identified during the iterative process of developing the minimum data set.     

Contributions of common sources of polycyclic aromatic hydrocarbons to soil contamination

Asphalt products, particularly sealants, are prepared using petroleum products that contain a com‐plex mixture of aliphatic and aromatic hydrocarbons, including polycyclic aromatic hydrocarbons (PAHs). Clearly, these products are ubiquitous in urban environments, which raises an issue regard‐ing the potential for PAHs to be transported from parking lots to underlying or adjacent soil, surface‐water bodies, or groundwater. Based on a literature review, there are limited studies focus‐ing on this issue; however, the studies that have been published have fascinating conclusions. The literature shows, as expected, that asphalt‐based products contain PAHs. The highest PAH concen‐trations are present in asphalt sealants, particularly those manufactured using coal tar. Furthermore, due to the low solubility and high partition coefficients of PAHs, the potential for PAHs to leach from asphalt surfaces is negligible, which has been confirmed by leachability studies. Thus, there is little risk that PAHs will be present in stormwater runoff or leach into groundwater from asphalt‐paved areas in a dissolved form. However, asphalt pavement and sealants produce particulate matter that can contain concentrations of PAHs in the sub‐percent range (100s to 1,000s mg/kg total PAHs) that is transported in stormwater runoff. Some studies show that this can cause soil and sediment con‐tamination with total PAH concentrations in the range of 1 to 10 mg/kg. From a remediation per‐spective, many site cleanups are conducted to remediate the presence of PAHs to cleanup goals below 1 mg/kg or, in some cases, 0.1 mg/kg or lower. From a total risk perspective, remediating sites to low PAH cleanup goals may be unwarranted in light of the risk of transportable PAHs produced from paved parking surfaces. In other words, is it reasonable to conduct a cleanup to remediate low PAH concentrations and then redevelop the area with asphalt pavement and sealant, which may pose a greater PAH‐related risk? © 2006 Wiley Periodicals, Inc.     

A stochastic model for estimating sustainable limits to wildlife mortality in a changing world

Human-caused mortality of wildlife is a pervasive threat to biodiversity. Assessing the population-level impact of fisheries bycatch and other human-caused mortality of wildlife has typically relied upon deterministic methods. However, population declines are often accelerated by stochastic factors that are not accounted for in such conventional methods. Building on the widely applied potential biological removal (PBR) equation, we devised a new population modeling approach for estimating sustainable limits to human-caused mortality and applied it in a case study of bottlenose dolphins affected by capture in an Australian demersal otter trawl fishery. Our approach, termed sustainable anthropogenic mortality in stochastic environments (SAMSE), incorporates environmental and demographic stochasticity, including the dependency of offspring on their mothers. The SAMSE limit is the maximum number of individuals that can be removed without causing negative stochastic population growth. We calculated a PBR of 16.2 dolphins per year based on the best abundance estimate available. In contrast, the SAMSE model indicated that only 2.3–8.0 dolphins could be removed annually without causing a population decline in a stochastic environment. These results suggest that reported bycatch rates are unsustainable in the long term, unless reproductive rates are consistently higher than average. The difference between the deterministic PBR calculation and the SAMSE limits showed that deterministic approaches may underestimate the true impact of human-caused mortality of wildlife. This highlights the importance of integrating stochasticity when evaluating the impact of bycatch or other human-caused mortality on wildlife, such as hunting, lethal control measures, and wind turbine collisions. Although population viability analysis (PVA) has been used to evaluate the impact of human-caused mortality, SAMSE represents a novel PVA framework that incorporates stochasticity for estimating acceptable levels of human-caused mortality. It offers a broadly applicable, stochastic addition to the demographic toolbox to evaluate the impact of human-caused mortality on wildlife.     

Pesticide and agro-ecological transition: assessing the environmental and human impacts of pesticides and limiting their use

Environmental Science and Pollution Research -     

Thermal dependency of burrowing in three species within the bivalve genus <Emphasis Type="Italic">Laternula</Emphasis>: a latitudinal comparison

The upper thermal limits for burrowing and survival were compared with micro-habitat temperature for anomalodesmatan clams: Laternula elliptica (Antarctica, 67°S); Laternula recta, (temperate Australia, 38°S) and Laternula truncata (tropical Singapore, 1°N). Lethal limits (LT₅₀) were higher than burrowing limits (BT₅₀) in L. elliptica (7.5–9.0 and 2.2°C) and L. recta (winter, 32.8–36.8 and 31.1–32.8°C) but the same range for L. truncata (33.0–35.0 and 33.4–34.9°C). L. elliptica and L. truncata had a BT₅₀ 0.4 and 2.4–3.9°C, respectively, above their maximum experienced temperature. L. recta, which experience solar heating during midday low tides, had a BT₅₀ 0.7–2.4°C below and a range for LT₅₀ that spanned their predicted environmental maximum (33.5°C). L. recta showed no seasonal difference in LT₅₀ or BT₅₀. Our single genus comparisons contrast with macrophysiological studies showing that temperate species cope better with elevated temperatures. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

High connectivity among locally adapted populations of a marine fish (Menidia menidia)

Patterns of connectivity are important in understanding the geographic scale of local adaptation in marine populations. While natural selection can lead to local adaptation, high connectivity can diminish the potential for such adaptation to occur. Connectivity, defined as the exchange of individuals among subpopulations, is presumed to be significant in most marine species due to life histories that include widely dispersive stages. However, evidence of local adaptation in marine species, such the Atlantic silverside, Menidia menidia, raises questions concerning the degree of connectivity. We examined geochemical signatures in the otoliths, or ear bones, of adult Atlantic silversides collected in 11 locations along the northeastern coast of the United States from New Jersey to Maine in 2004 and eight locations in 2005 using laser ablation inductively coupled plasma mass spectrometry (ICP-MS) and isotope ratio monitoring mass spectrometry (irm-MS). These signatures were then compared to baseline signatures of juvenile fish of known origin to determine natal origin of these adult fish. We then estimated migration distances and the degree of mixing from these data. In both years, fish generally had the highest probability of originating from the same location in which they were captured (0.01-0.80), but evidence of mixing throughout the sample area was present. Furthermore, adult M. menidia exhibit highly dispersive behavior with some fish migrating over 700 km. The probability of adult fish returning to natal areas differed between years, with the probability being, on average, 0.2 higher in the second year. These findings demonstrate that marine species with largely open populations are capable of local adaptation despite apparently high gene flow.     

A case for quantile regression in behavioral ecology: getting more out of flight initiation distance data

A central goal of behavioral ecology is to quantify and explain variation in behavior. While much previous work has focused on the differences in mean behavior across groups or treatments, we present a complementary approach studying changes in the distribution of the response variable. This is important because changes in the edges of a distribution may be more informative than changes in the mean if behavior at the edges of a distribution better reflects behavioral constraints. Quantile regression estimates the rate of change of conditional quantiles of a response variable and thus allows the study of changes in any part of its distribution. Although quantile regression is gaining popularity in the ecological literature, it is strikingly unused in behavioral ecology. Here, we demonstrate the usefulness of this method by analyzing the relationship between the starting distance (SD) at which an observer approach a focal animal and its flight initiation distance (FID, the distance between the observer and the animal when it decides to flee). In particular, we used a simple model of flight initiation distance to show that in most situations ordinary least-square regression cannot be used to analyse the SD–FID relationship. Quantile regression conducted on the lowest quantiles appears more robust and we applied this approach to data from four bird species. Overall, changes in the lowest FID values appeared to be the most informative to determine if a species displays a “flush early” strategy, a strategy which has been hypothesized to be a general rule. We hope this example will bring quantile regression to the attention of behavioral ecologists as a valuable tool to add to their statistical toolbox.