Contributions of parasites to energy flow through a stream food web

Parasites are increasingly recognized for their significant roles in shaping community structure and ecosystem dynamics. Due to their cryptic nature, however, they are often neglected in measurements of energy flow and biomass in ecosystems. Past studies have demonstrated that parasites directly contribute to the flow of energy through marine estuaries and freshwater wetlands, yet it remains unknown how broadly applicable these findings are across other ecosystems. The aim of our study was to quantify the role of trematode parasites in the flow of energy through freshwater streams, using biomass as a proxy for energy flow. To accomplish this, we quantified the density and size distributions of aquatic organisms using quadrat and surber samplers within three Oregon steams (n = 9 field sites total) during the summer months. We then used length-to-mass regressions to estimate the free-living biomass of each organism per unit area. To estimate trematode biomass, we dissected freshwater snail hosts, Juga plicifera, quantifying infection prevalence and trematode mass as a function of host body size for each trematode taxon. Results indicated that Juga snails dominated the standing crop biomass of aquatic invertebrates, constituting an average of 87.5% of the total invertebrate biomass across the three streams (mean = 3.77 g m2). The other most important groups included Coleoptera (0.0315 g m2), Plecoptera (0.0138 g m2), Trichoptera (0.0129 g m2), and Odonata (0.0128 g m2). We identified five trematode taxa that infected Juga snails at our field sites (Metagonimoides oregonensis, Nanophyetus salmincola, Acanthatrium oregonense, Derogenidae spp., and Opecoilidae spp.). Trematode tissue comprised an average of 31% of the total body mass of each infected snail at the individual level, with a positive correlation between snail size and percent trematode mass. Trematode biomass ranged from 4% to 17% of the total population-level snail biomass, averaging 0.392 g m2 across the sites. Trematode biomass exceeded all taxon-specific and total free-living insect biomass (mean = 0.096 g m2). Our results suggest that trematodes are capable of surpassing the roles of many major free-living invertebrates in the movement of energy through these stream food webs. Furthermore, because few aquatic predators consume Juga snails, trematodes potentially alter pathways of energy flow by routing snail production through parasites and into other components of the aquatic food web

Finding Nature in Your Neighborhood: A Field Mapping Protocol for Community Based Assessment of Greenspace Access

The Audubon Society of Portland and PSU Capstone students developed the Greenspace Access Point Field Mapping Protocol during the summer of 2013. The protocol was developed as a tool for a community-based approach to inventorying open space access points and to generate more accurate information on open space access in the Portland-Metro region

What drives interaction strengths in complex food webs? A test with feeding rates of a generalist stream predator

Describing the mechanisms that drive variation in species interaction strengths is central to understanding, predicting, and managing community dynamics. Multiple factors have been linked to trophic interaction strength variation, including species densities, species traits, and abiotic factors. Yet most empirical tests of the relative roles of multiple mechanisms that drive variation have been limited to simplified experiments that may diverge from the dynamics of natural food webs. Here, we used a field-based observational approach to quantify the roles of prey density, predator density, predator-prey body-mass ratios, prey identity, and abiotic factors in driving variation in feeding rates of reticulate sculpin (Cottus perplexus). We combined data on over 6,000 predator-prey observations with prey identification time functions to estimate 289 prey-specific feeding rates at nine stream sites in Oregon. Feeding rates on 57 prey types showed an approximately log-normal distribution, with few strong and many weak interactions. Model selection indicated that prey density, followed by prey identity, were the two most important predictors of prey-specific sculpin feeding rates. Feeding rates showed a positive relationship with prey taxon densities that was inconsistent with predator saturation predicted by current functional response models. Feeding rates also exhibited four orders-of-magnitude in variation across prey taxonomic orders, with the lowest feeding rates observed on prey with significant anti-predator defenses. Body-mass ratios were the third most important predictor variable, showing a hump-shaped relationship with the highest feeding rates at intermediate ratios. Sculpin density was negatively correlated with feeding rates, consistent with the presence of intraspecific predator interference. Our results highlight how multiple co-occurring drivers shape trophic interactions in nature and underscore ways in which simplified experiments or reliance on scaling laws alone may lead to biased inferences about the structure and dynamics of species-rich food webs

Inequalities in noise will affect urban wildlife

Understanding the extent to which systemic biases influence local ecological communities is essential for developing just and equitable environmental practices. With over 270 million people across the United States living in urban areas, understanding the socio-ecological consequences of racially-targeted zoning, such as redlining, provides crucial information for urban planning. There is a growing body of literature documenting the relationships between redlining and disparities in the distribution of environmental harms and goods, including inequities in green space cover and pollutant exposure. Yet, it remains unknown whether noise pollution is also inequitably distributed, and whether inequitable noise is an important driver of ecological change in urban environments. We conducted 1) a spatial analysis of urban noise to determine the extent to which noise overlaps with the distribution of redlining categories and 2) a systematic literature review to summarize the effects of noise on wildlife in urban landscapes. We found strong evidence that noise is inequitably distributed in cities across the United States, and that inequitable noise may drive complex biological responses across diverse urban wildlife. These findings lay a foundation for future research that advances acoustic and urban ecology by centering equity and challenging systems of oppression.Data can be viewed using any software that can open a .csv file. Code can be viewed using any software that can open a .txt file.Funding provided by: Colorado State UniversityCrossref Funder Registry ID: http://dx.doi.org/10.13039/100007235Award Number:Spatial Analysis of Urban Noise Pollution To evaluate noise exposure across HOLC redlining grades for 83 U.S. cities in our study, we acquired spatial data on the distribution of HOLC grades across U.S. cities from the Mapping Inequality Project. We also acquired data on road, rail, and aircraft noise (hereafter transportation noise models), from the U.S. Department of Transportation, National Transportation Noise Map 2018. The transportation noise models represent potential exposure to transportation noise reported on a decibel scale in a 30m x 30m pixel resolution. Here noise represents the average noise energy produced by road, rail, and aviation networks over a 24-hour period, measured in A-weighted decibels (dBA) (LAeq, 24h) at sampling locations deployed across a uniform grid in each city at an elevation of 1.5 m above ground level. Noise levels below 35 dBA are assumed to have minimal negative impacts to humans and the environment and thus are represented with null values in the transportation noise models. For each HOLC grade and each city, we used zonal statistics in ArcGIS Desktop v. 10.7 to calculate descriptive statistics (median, minimum, maximum, area) for the 30m x 30m pixels in the transportation noise models with non-null noise values (i.e., values > 35 dBA). We used the resulting zonal statistics estimates to calculate an area-corrected measure of excess noise: N = (r * Md)/a where N is excess noise in each HOLC grade (with units of dBA/900 m2); r is the area covered by the 30m x 30m pixels with noise values >35 dBA across all polygons of the same HOLC grade in each city; Md is the median transportation noise value (in dBA) for those same pixels; and a is the total area of all polygons of the same HOLC grade in each city. Literature Review Methodology To assess the effects of noise on wildlife in urban environments, we conducted a literature review using Thompson's ISI Web of Science and adapting the methods of Shannon et al. (2016). We adjusted Shannon et al. (2016) search criteria to include urban phrases, resulting in the following search terms (TS=(WILDLIFE OR ANIMAL OR MAMMAL OR REPTILE OR AMPHIBIAN OR BIRD OR FISH OR INVERTEBRATE) AND TS=(NOISE OR SONAR) AND TS=(CITY OR *URBAN OR METROPOLITAN)). We only selected papers published between 1990 and 23 June 2021 (i.e., the date we conducted our search) within the ISI Web of Science categories of 'Acoustics', 'Zoology', 'Ecology', 'Environmental Sciences', 'Ornithology', 'Biodiversity Conservation', 'Evolutionary Biology', and 'Marine Freshwater Biology'. This returned 691 peer-reviewed papers, which we filtered so only empirical studies focused on documenting the effects of anthropogenic noise on wildlife in urban or suburban ecosystems or the effects of urban noise on wildlife in rural environments were included in the final data set. We excluded reviews, meta-analyses, methods papers, and research that took place outside of urban or suburban areas where the noise was not explicitly denoted as urban (e.g., omitted studies that measured traffic noise by parks and reserves in rural areas). For the 241 articles previously analyzed in Shannon et al. (2016), one of our authors reviewed each paper to determine which studies were focused on urban noise. We also verified the noise levels that caused a significant biological response, noting each noise level if multiple responses were recorded. For any new articles published since the Shannon et al. (2016) dataset or those published between 1990 and 2013 but not reviewed by Shannon et al. (2016) (n = 96), two of our authors reviewed each paper to first determine which studies met our criteria and then compiled data on a number of variables of interest, including the noise levels and their resulting biological responses that were statistically significant. For this subset of papers, one author was randomly assigned a list of papers and then a second author was randomly assigned to assess the accuracy of the data collected by the first author. Any discrepancies were discussed as a group until an agreement was reached. Noise categories (environmental, transportation, industrial, multiple, other) were chosen for each paper by noting the explicitly stated source or description of urban noise described in the methodology. Noise levels and their units were reported for each paper, with only noise levels reported in decibels (dB) being used in data analysis. We recorded the sound metric used (i.e., SPL, SPL Max, Leq) for each paper and also recorded the weightings for each noise level

Inequalities in noise will affect urban wildlife

Understanding the extent to which systemic biases influence local ecological communities is essential for developing just and equitable environmental practices. With over 270 million people across the United States living in urban areas, understanding the socio-ecological consequences of racially-targeted zoning, such as redlining, provides crucial information for urban planning. There is a growing body of literature documenting the relationships between redlining and disparities in the distribution of environmental harms and goods, including inequities in green space cover and pollutant exposure. Yet, it remains unknown whether noise pollution is also inequitably distributed, and whether inequitable noise is an important driver of ecological change in urban environments. We conducted 1) a spatial analysis of urban noise to determine the extent to which noise overlaps with the distribution of redlining categories and 2) a systematic literature review to summarize the effects of noise on wildlife in urban landscapes. We found strong evidence that noise is inequitably distributed in cities across the United States, and that inequitable noise may drive complex biological responses across diverse urban wildlife. These findings lay a foundation for future research that advances acoustic and urban ecology by centering equity and challenging systems of oppression.Data can be viewed using any software that can open a .csv file. Code can be viewed using any software that can open a .txt file.Funding provided by: Colorado State UniversityCrossref Funder Registry ID: http://dx.doi.org/10.13039/100007235Award Number:Spatial Analysis of Urban Noise Pollution To evaluate noise exposure across HOLC redlining grades for 83 U.S. cities in our study, we acquired spatial data on the distribution of HOLC grades across U.S. cities from the Mapping Inequality Project. We also acquired data on road, rail, and aircraft noise (hereafter transportation noise models), from the U.S. Department of Transportation, National Transportation Noise Map 2018. The transportation noise models represent potential exposure to transportation noise reported on a decibel scale in a 30m x 30m pixel resolution. Here noise represents the average noise energy produced by road, rail, and aviation networks over a 24-hour period, measured in A-weighted decibels (dBA) (LAeq, 24h) at sampling locations deployed across a uniform grid in each city at an elevation of 1.5 m above ground level. Noise levels below 35 dBA are assumed to have minimal negative impacts to humans and the environment and thus are represented with null values in the transportation noise models. For each HOLC grade and each city, we used zonal statistics in ArcGIS Desktop v. 10.7 to calculate descriptive statistics (median, minimum, maximum, area) for the 30m x 30m pixels in the transportation noise models with non-null noise values (i.e., values > 35 dBA). We used the resulting zonal statistics estimates to calculate an area-corrected measure of excess noise: N = (r * Md)/a where N is excess noise in each HOLC grade (with units of dBA/900 m2); r is the area covered by the 30m x 30m pixels with noise values >35 dBA across all polygons of the same HOLC grade in each city; Md is the median transportation noise value (in dBA) for those same pixels; and a is the total area of all polygons of the same HOLC grade in each city. Literature Review Methodology To assess the effects of noise on wildlife in urban environments, we conducted a literature review using Thompson's ISI Web of Science and adapting the methods of Shannon et al. (2016). We adjusted Shannon et al. (2016) search criteria to include urban phrases, resulting in the following search terms (TS=(WILDLIFE OR ANIMAL OR MAMMAL OR REPTILE OR AMPHIBIAN OR BIRD OR FISH OR INVERTEBRATE) AND TS=(NOISE OR SONAR) AND TS=(CITY OR *URBAN OR METROPOLITAN)). We only selected papers published between 1990 and 23 June 2021 (i.e., the date we conducted our search) within the ISI Web of Science categories of 'Acoustics', 'Zoology', 'Ecology', 'Environmental Sciences', 'Ornithology', 'Biodiversity Conservation', 'Evolutionary Biology', and 'Marine Freshwater Biology'. This returned 691 peer-reviewed papers, which we filtered so only empirical studies focused on documenting the effects of anthropogenic noise on wildlife in urban or suburban ecosystems or the effects of urban noise on wildlife in rural environments were included in the final data set. We excluded reviews, meta-analyses, methods papers, and research that took place outside of urban or suburban areas where the noise was not explicitly denoted as urban (e.g., omitted studies that measured traffic noise by parks and reserves in rural areas). For the 241 articles previously analyzed in Shannon et al. (2016), one of our authors reviewed each paper to determine which studies were focused on urban noise. We also verified the noise levels that caused a significant biological response, noting each noise level if multiple responses were recorded. For any new articles published since the Shannon et al. (2016) dataset or those published between 1990 and 2013 but not reviewed by Shannon et al. (2016) (n = 96), two of our authors reviewed each paper to first determine which studies met our criteria and then compiled data on a number of variables of interest, including the noise levels and their resulting biological responses that were statistically significant. For this subset of papers, one author was randomly assigned a list of papers and then a second author was randomly assigned to assess the accuracy of the data collected by the first author. Any discrepancies were discussed as a group until an agreement was reached. Noise categories (environmental, transportation, industrial, multiple, other) were chosen for each paper by noting the explicitly stated source or description of urban noise described in the methodology. Noise levels and their units were reported for each paper, with only noise levels reported in decibels (dB) being used in data analysis. We recorded the sound metric used (i.e., SPL, SPL Max, Leq) for each paper and also recorded the weightings for each noise level

Inequalities in noise will affect urban wildlife

Understanding how systemic biases influence local ecological communities is essential for developing just and equitable environmental practices that prioritize both human and wildlife well-being. With over 270 million residents inhabiting urban areas in the United States, the socioecological consequences of racially targeted zoning, such as redlining, need to be considered in urban planning. There is a growing body of literature documenting the relationships between redlining and the inequitable distribution of environmental harms and goods, green space cover and pollutant exposure. However, it remains unknown whether historical redlining affects the distribution of urban noise or whether inequitable noise drives an ecological change in urban environments. Here we conducted a spatial analysis of how urban noise corresponds to the distribution of redlining categories and a systematic literature review to summarize the effects of noise on wildlife in urban landscapes. We found strong evidence to indicate that noise is inequitably distributed in redlined urban communities across the United States, and that inequitable noise may drive complex biological responses across diverse urban wildlife, reinforcing the interrelatedness of socioecological outcomes. These findings lay a foundation for future research that advances relationships between acoustic and urban ecology through centring equity and challenging systems of oppression in wildlife studies