Using citizen science data to identify the sensitivity of species to human land use

Conservation practitioners must contend with an increasing array of threats that affect biodiversity. Citizen scientists can provide timely and expansive information for addressing these threats across large scales, but their data may contain sampling biases. We used randomization procedures to account for possible sampling biases in opportunistically reported citizen science data to identify species’ sensitivities to human land use. We analyzed 21,044 records of 143 native reptile and amphibian species reported to the Carolina Herp Atlas from North Carolina and South Carolina between 1 January 1990 and 12 July 2014. Sensitive species significantly associated with natural landscapes were 3.4 times more likely to be legally protected or treated as of conservation concern by state resource agencies than less sensitive species significantly associated with human‐dominated landscapes. Many of the species significantly associated with natural landscapes occurred primarily in habitats that had been nearly eradicated or otherwise altered in the Carolinas, including isolated wetlands, longleaf pine savannas, and Appalachian forests. Rare species with few reports were more likely to be associated with natural landscapes and 3.2 times more likely to be legally protected or treated as of conservation concern than species with at least 20 reported occurrences. Our results suggest that opportunistically reported citizen science data can be used to identify sensitive species and that species currently restricted primarily to natural landscapes are likely at greatest risk of decline from future losses of natural habitat. Our approach demonstrates the usefulness of citizen science data in prioritizing conservation and in helping practitioners address species declines and extinctions at large extents.     

Tropical amphibians in shifting thermal landscapes under land‐use and climate change

Land‐cover and climate change are both expected to alter species distributions and contribute to future biodiversity loss. However, the combined effects of land‐cover and climate change on assemblages, especially at the landscape scale, remain understudied. Lowland tropical amphibians may be particularly susceptible to changes in land cover and climate warming because many species have narrow thermal safety margins resulting from air and body temperatures that are close to their critical thermal maxima (CT_max). We examined how changing thermal landscapes may alter the area of thermally suitable habitat (TSH) for tropical amphibians. We measured microclimates in 6 land‐cover types and CT_max of 16 frog species in lowland northeastern Costa Rica. We used a biophysical model to estimate core body temperatures of frogs exposed to habitat‐specific microclimates while accounting for evaporative cooling and behavior. Thermally suitable habitat area was estimated as the portion of the landscape where species CT_max exceeded their habitat‐specific maximum body temperatures. We projected changes in TSH area 80 years into the future as a function of land‐cover change only, climate change only, and combinations of land‐cover and climate‐change scenarios representing low and moderate rates of change. Projected decreases in TSH area ranged from 16% under low emissions and reduced forest loss to 30% under moderate emissions and business‐as‐usual land‐cover change. Under a moderate emissions scenario (A1B), climate change alone contributed to 1.7‐ to 4.5‐fold greater losses in TSH area than land‐cover change only, suggesting that future decreases in TSH from climate change may outpace structural habitat loss. Forest‐restricted species had lower mean CT_max than species that occurred in altered habitats, indicating that thermal tolerances will likely shape assemblages in changing thermal landscapes. In the face of ongoing land‐cover and climate change, it will be critical to consider changing thermal landscapes in strategies to conserve ectotherm species.     

Assessing the likely effectiveness of multispecies management for imperiled desert fishes with niche overlap analysis

A critical decision in species conservation is whether to target individual species or a complex of ecologically similar species. Management of multispecies complexes is likely to be most effective when species share similar distributions, threats, and response to threats. We used niche overlap analysis to assess ecological similarity of 3 sensitive desert fish species currently managed as an ecological complex. We measured the amount of shared distribution of multiple habitat and life history parameters between each pair of species. Habitat use and multiple life history parameters, including maximum body length, spawning temperature, and longevity, differed significantly among the 3 species. The differences in habitat use and life history parameters among the species suggest they are likely to respond differently to similar threats and that most management actions will not benefit all 3 species equally. Habitat restoration, frequency of stream dewatering, non‐native species control, and management efforts in tributaries versus main stem rivers are all likely to impact each of the species differently. Our results demonstrate that niche overlap analysis provides a powerful tool for assessing the likely effectiveness of multispecies versus single‐species conservation plans. Evaluación de la Posible Efectividad del Manejo Multi‐Especie paraPeces de Desierto en Peligro Mediante el Análisis de Traslape de Nichos     

Improving credibility and transparency of conservation impact evaluations through the partial identification approach

The fundamental challenge of evaluating the impact of conservation interventions is that researchers must estimate the difference between the outcome after an intervention occurred and what the outcome would have been without it (counterfactual). Because the counterfactual is unobservable, researchers must make an untestable assumption that some units (e.g., organisms or sites) that were not exposed to the intervention can be used as a surrogate for the counterfactual (control). The conventional approach is to make a point estimate (i.e., single number along with a confidence interval) of impact, using, for example, regression. Point estimates provide powerful conclusions, but in nonexperimental contexts they depend on strong assumptions about the counterfactual that often lack transparency and credibility. An alternative approach, called partial identification (PI), is to first estimate what the counterfactual bounds would be if the weakest possible assumptions were made. Then, one narrows the bounds by using stronger but credible assumptions based on an understanding of why units were selected for the intervention and how they might respond to it. We applied this approach and compared it with conventional approaches by estimating the impact of a conservation program that removed invasive trees in part of the Cape Floristic Region. Even when we used our largest PI impact estimate, the program's control costs were 1.4 times higher than previously estimated. PI holds promise for applications in conservation science because it encourages researchers to better understand and account for treatment selection biases; can offer insights into the plausibility of conventional point‐estimate approaches; could reduce the problem of advocacy in science; might be easier for stakeholders to agree on a bounded estimate than a point estimate where impacts are contentious; and requires only basic arithmetic skills.     

Short-Term Forecasting of Nitrogen Dioxide (NO<Subscript>2</Subscript>) Levels Using a Hybrid Statistical and Air Mass History Modelling Approach

A novel hybrid model has been developed to support the provision of real-time air quality forecasts. Statistical techniques have been applied in parallel with air mass history modelling to provide an efficient and accurate forecasting system with the ability to identify high NO₂ events, which tend to be the episodes of most significance in Ireland. Air mass history modelling and k-means clustering are used to identify air mass types that lead to high NO₂ levels in Ireland. Trajectory matching techniques allow data associated with these air masses to be partitioned during model development. Non-parametric regression (NPR) has been applied to describe nonlinear variations in concentration levels with wind speed, direction and season and produce a set of linearized factors which, together with other meteorological variables, are employed as inputs to a multiple linear regression. The model uses an innovative integrated approach to combine the NPR with the air mass history modelling results. On validation, a correlation coefficient of 0.75 was obtained, and 91 % of daily maximum (hourly averaged) NO₂ predictions were within a factor of two of the measured value. High pollution events were well captured, as indicated by strong agreement between measured and modelled high percentile values. The model requires only simple input data, does not require an emission inventory and utilises very low computational resources. It represents an accurate and efficient means of producing real-time air quality forecasts and, when used in combination with forecaster experience, is a useful tool for identifying periods of poor air quality 24 h in advance. The hybrid approach outlined in this paper can easily be applied to produce high-quality forecasts of both NO₂ and additional pollutants at new locations/countries where historical monitoring data are available.     

Accounting for adaptive capacity and uncertainty in assessments of species’ climate‐change vulnerability

Climate‐change vulnerability assessments (CCVAs) are valuable tools for assessing species’ vulnerability to climatic changes, yet failure to include measures of adaptive capacity and to account for sources of uncertainty may limit their effectiveness. We took a more comprehensive approach that incorporates exposure, sensitivity, and capacity to adapt to climate change. We applied our approach to anadromous steelhead trout (Oncorhynchus mykiss) and nonanadromous bull trout (Salvelinus confluentus), threatened salmonids within the Columbia River Basin (U.S.A.). We quantified exposure on the basis of scenarios of future stream temperature and flow, and we represented sensitivity and capacity to adapt to climate change with metrics of habitat quality, demographic condition, and genetic diversity. Both species were found to be highly vulnerable to climate change at low elevations and in their southernmost habitats. However, vulnerability rankings varied widely depending on the factors (climate, habitat, demographic, and genetic) included in the CCVA and often differed for the 2 species at locations where they were sympatric. Our findings illustrate that CCVA results are highly sensitive to data inputs and that spatial differences can complicate multispecies conservation. Based on our results, we suggest that CCVAs be considered within a broader conceptual and computational framework and be used to refine hypotheses, guide research, and compare plausible scenarios of species’ vulnerability to climate change.     

Future habitat loss and extinctions driven by land‐use change in biodiversity hotspots under four scenarios of climate‐change mitigation

Numerous species have been pushed into extinction as an increasing portion of Earth's land surface has been appropriated for human enterprise. In the future, global biodiversity will be affected by both climate change and land‐use change, the latter of which is currently the primary driver of species extinctions. How societies address climate change will critically affect biodiversity because climate‐change mitigation policies will reduce direct climate‐change impacts; however, these policies will influence land‐use decisions, which could have negative impacts on habitat for a substantial number of species. We assessed the potential impact future climate policy could have on the loss of habitable area in biodiversity hotspots due to associated land‐use changes. We estimated past extinctions from historical land‐use changes (1500–2005) based on the global gridded land‐use data used for the Intergovernmental Panel on Climate Change Fifth Assessment Report and habitat extent and species data for each hotspot. We then estimated potential extinctions due to future land‐use changes under alternative climate‐change scenarios (2005–2100). Future land‐use changes are projected to reduce natural vegetative cover by 26‐58% in the hotspots. As a consequence, the number of additional species extinctions, relative to those already incurred between 1500 and 2005, due to land‐use change by 2100 across all hotspots ranged from about 220 to 21000 (0.2% to 16%), depending on the climate‐change mitigation scenario and biological factors such as the slope of the species–area relationship and the contribution of wood harvest to extinctions. These estimates of potential future extinctions were driven by land‐use change only and likely would have been higher if the direct effects of climate change had been considered. Future extinctions could potentially be reduced by incorporating habitat preservation into scenario development to reduce projected future land‐use changes in hotspots or by lessening the impact of future land‐use activities on biodiversity within hotspots.     

Sensitivity of streamflow and microbial water quality to future climate and land use change in the West of Ireland

Regional Environmental Change - This study applied catchment modeling to examine the potential effects of climate change and future land management variations on streamflow and microbial transport...     

Examining the coupling of carbon and nitrogen cycles in Appalachian streams: the role of dissolved organic nitrogen

Although regional and global models of nitrogen (N) cycling typically focus on nitrate, dissolved organic nitrogen (DON) is the dominant form of nitrogen export from many watersheds and thus the dominant form of dissolved N in many streams. Our understanding of the processes controlling DON export from temperate forests is poor. In pristine systems, where biological N limitation is common, N contained in recalcitrant organic matter (OM) can dominate watershed N losses. This recalcitrant OM often has moderately constrained carbon:nitrogen (C:N) molar ratios (approximately 25-55) and therefore, greater DON losses should be observed in sites where there is greater total dissolved organic carbon (DOC) loss. In regions where anthropogenic N pollution is high, it has been suggested that increased inorganic N availability can reduce biological demand for organic N and therefore increase watershed DON losses. This would result in a positive correlation between inorganic and organic N concentrations across sites with varying N availability. In four repeated synoptic surveys of stream water chemistry from forested watersheds along an N loading gradient in the southern Appalachians, we found surprisingly little correlation between DON and DOC concentrations. Further, we found that DON concentrations were always significantly correlated with watershed N loading and stream water [NO3-] but that the direction of this relationship was negative in three of the four surveys. The C:N molar ratio of dissolved organic matter (DOM) in streams draining watersheds with high N deposition was very high relative to other freshwaters. This finding, together with results from bioavailability assays in which we directly manipulated C and N availabilities, suggests that heterotrophic demand for labile C can increase as a result of dissolved inorganic N (DIN) loading, and that heterotrophs can preferentially remove N-rich molecules from DOM. These results are inconsistent with the two prevailing hypotheses that dominate interpretations of watershed DON loss. Therefore, we propose a new hypothesis, the indirect carbon control hypothesis, which recognizes that heterotrophic demand for N-rich DOM can keep stream water DON concentrations low when N is not limiting and heterotrophic demand for labile C is high.     

Local physical habitat quality cloud the effect of predicted pesticide runoff from agricultural land in Danish streams

The combination of intensive agricultural activities and the close connectivity between land and stream emphasise the potential risk of pesticide exposure in Danish streams. Benthic macroinvertebrates are applied in the assessment of stream ecological status, and some sensitive species have been shown to respond strongly to brief pulses of pesticide contamination. In this study we investigate the impact of agriculturally derived pesticides on stream macroinvertebrate communities in Denmark. As a measure of toxic pressure we apply the Runoff Potential. We investigated a total of 212 streams. These were grouped into distinct classes according to the magnitude of pesticide contamination in the period from 2003-2006. A total of 24 different macroinvertebrate indices were applied to detect effects of pesticide runoff (e.g. the SPEAR-index and the number of EPT taxa). We found high predicted pesticide runoff in 39% of the streams, but we found no significant effect of predicted pesticide exposure on stream macroinvertebrate indices. We, additionally, examined the influence of a series of environmental parameters ranging from site scale to catchment scale on the macroinvertebrate community. Relative proportions of gravel, sand and silt in bed sediments explained most of the variation in macroinvertebrate indices as well as the upstream riparian habitat quality. We suggest that the Runoff Potential model overestimate pesticide runoff contamination in Danish streams due the presence of buffer strips enforced by Danish legislation. When pesticide runoff contamination is low to moderate, poor physical properties (indirectly related to agricultural activity) are the main impediment for the ecological quality of Danish streams.