In arid and semiarid regions, where few if any trees are native, city trees are largely human planted. Societal factors such as resident preferences for tree traits, nursery offerings, and ...neighborhood characteristics are potentially key drivers of urban tree community composition and diversity, however, they remain critically understudied. We investigated patterns of urban tree structure in residential neighborhoods of the Salt Lake Valley, Utah, combining biological variables, such as neighborhood and plant nursery tree species and trait composition, and sociological data comprised of resident surveys and U.S. Census data. We sampled nine neighborhoods that varied in household income and age of homes. We found more tree species were offered in locally owned nurseries compared with mass merchandiser stores and yard trees at private residences were more diverse than public street trees in the same neighborhoods. There were significant differences among neighborhoods in street and yard tree composition. Newer neighborhoods differed from older neighborhoods in street tree species composition and trait diversity, while neighborhoods varying in affluence differed in yard tree composition. Species richness of yard trees was positively correlated with neighborhood household income, while species richness of street trees was negatively correlated with home age of neighborhood residences. Tree traits differed across neighborhoods of varying ages, suggesting different tree availability and preferences over time. Last, there was a positive correlation between resident preferences for tree attributes and the number of trees that had those attributes both in residential yards and in nursery offerings. Strong relationships between social variables and urban tree composition provides evidence that resident preferences and nursery offerings affect patterns of biodiversity in cities across Salt Lake Valley. These findings can be applied toward efforts to increase taxonomic and functional diversity of city trees in semiarid regions in ways that will also provide ecosystem services of most interest to residents.
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BFBNIB, FZAB, GIS, IJS, INZLJ, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZRSKP
Demystifying dominant species Avolio, Meghan L.; Forrestel, Elisabeth J.; Chang, Cynthia C. ...
New phytologist,
08/2019, Volume:
223, Issue:
3
Journal Article
Peer reviewed
Open access
The pattern of a few abundant species and many rarer species is a defining characteristic of communities worldwide. These abundant species are often referred to as dominant species. Yet, despite ...their importance, the term dominant species is poorly defined and often used to convey different information by different authors. Based on a review of historical and contemporary definitions we develop a synthetic definition of dominant species. This definition incorporates the relative local abundance of a species, its ubiquity across the landscape, and its impact on community and ecosystem properties. A meta-analysis of removal studies shows that the loss of species identified as dominant by authors can significantly impact ecosystem functioning and community structure. We recommend two metrics that can be used jointly to identify dominant species in a given community and provide a roadmap for future avenues of research on dominant species. In our review, we make the case that the identity and effects of dominant species on their environments are key to linking patterns of diversity to ecosystem function, including predicting impacts of species loss and other aspects of global change on ecosystems.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Intensification of the global hydrological cycle, ranging from larger individual precipitation events to more extreme multiyear droughts, has the potential to cause widespread alterations in ...ecosystem structure and function. With evidence that the incidence of extreme precipitation years (defined statistically from historical precipitation records) is increasing, there is a clear need to identify ecosystems that are most vulnerable to these changes and understand why some ecosystems are more sensitive to extremes than others. To date, opportunistic studies of naturally occurring extreme precipitation years, combined with results from a relatively small number of experiments, have provided limited mechanistic understanding of differences in ecosystem sensitivity, suggesting that new approaches are needed. Coordinated distributed experiments (CDEs) arrayed across multiple ecosystem types and focused on water can enhance our understanding of differential ecosystem sensitivity to precipitation extremes, but there are many design challenges to overcome (e.g., cost, comparability, standardization). Here, we evaluate contemporary experimental approaches for manipulating precipitation under field conditions to inform the design of ‘Drought‐Net’, a relatively low‐cost CDE that simulates extreme precipitation years. A common method for imposing both dry and wet years is to alter each ambient precipitation event. We endorse this approach for imposing extreme precipitation years because it simultaneously alters other precipitation characteristics (i.e., event size) consistent with natural precipitation patterns. However, we do not advocate applying identical treatment levels at all sites – a common approach to standardization in CDEs. This is because precipitation variability varies >fivefold globally resulting in a wide range of ecosystem‐specific thresholds for defining extreme precipitation years. For CDEs focused on precipitation extremes, treatments should be based on each site's past climatic characteristics. This approach, though not often used by ecologists, allows ecological responses to be directly compared across disparate ecosystems and climates, facilitating process‐level understanding of ecosystem sensitivity to precipitation extremes.
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Causal inferences from experimental data are often justified based on treatment randomization. However, inferring causality from data also requires complementary causal assumptions, which have been ...formalized by scholars of causality but not widely discussed in ecology. While ecologists have recognized challenges to inferring causal relationships in experiments and developed solutions, they lack a general framework to identify and address them. We review four assumptions required to infer causality from experiments and provide design-based and statistically based solutions for when these assumptions are violated. We conclude that there is no clear demarcation between experimental and non-experimental designs. This insight can help ecologists design better experiments and remove barriers between experimental and observational scholarship in ecology.
Causal inferences require causal assumptions. To formalize the assumptions required to draw causal inferences from experimental data, scholars have leveraged insights about causal inference in observational settings.Even carefully designed experiments may face challenges in satisfying four important causal assumptions. Ecologists sometimes acknowledge and address these challenges but do not have a cohesive framework for understanding them.When the validity of a causal assumption is questionable, ecologists can apply design-based and statistically based solutions.Despite popular wisdom, no clear demarcation exists between experimental and non-experimental designs. When inferring causal relationships from data, experimentalists need to be just as careful as non-experimentalists in assessing the validity of their assumptions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In cities, humans directly and indirectly affect plant and wildlife communities. These human–species interactions are not included in traditional ecological approaches used to understand why and how ...organisms are distributed. Here, we incorporate human behaviors into urban community assembly theories and detail all the complex ways humans affect the dispersal, selection and persistence of species in cities. To do this, we integrate human behaviors and actions into traditional filter frameworks used to study community assembly. We use our framework to develop testable hypotheses to predict patterns of urban diversity as well as pose key considerations for future research. In order to have a predictive understanding of how urban biodiversity responds to environmental, social and land use change, it is necessary to better understand interactions between humans and other organisms.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Humans promote and inhibit other species on the urban landscape, shaping biodiversity patterns. Institutional racism may underlie the distribution of urban species by creating disproportionate ...resources in space and time. Here, we examine whether present‐day street tree occupancy, diversity, and composition in Baltimore, MD, USA, neighborhoods reflect their 1937 classification into grades of loan risk—from most desirable (A = green) to least desirable (D = “redlined”)—using racially discriminatory criteria. We find that neighborhoods that were redlined have consistently lower street tree α‐diversity and are nine times less likely to have large (old) trees occupying a viable planting site. Simultaneously, redlined neighborhoods were locations of recent tree planting activities, with a high occupancy rate of small (young) trees. However, the community composition of these young trees exhibited lower species turnover and reordering across neighborhoods compared to those in higher grades, due to heavy reliance on a single tree species. Overall, while the negative effects of redlining remain detectable in present‐day street tree communities, there are clear signs of recent investment. A strategy of planting diverse tree cohorts paired with investments in site rehabilitation and maintenance may be necessary if cities wish to overcome ecological feedbacks associated with legacies of environmental injustice.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Climate change is intensifying the hydrologic cycle and is expected to increase the frequency of extreme wet and dry years. Beyond precipitation amount, extreme wet and dry years may differ in other ...ways, such as the number of precipitation events, event size, and the time between events. We assessed 1614 long‐term (100 year) precipitation records from around the world to identify key attributes of precipitation regimes, besides amount, that distinguish statistically extreme wet from extreme dry years. In general, in regions where mean annual precipitation (MAP) exceeded 1000 mm, precipitation amounts in extreme wet and dry years differed from average years by ~40% and 30%, respectively. The magnitude of these deviations increased to >60% for dry years and to >150% for wet years in arid regions (MAP<500 mm). Extreme wet years were primarily distinguished from average and extreme dry years by the presence of multiple extreme (large) daily precipitation events (events >99th percentile of all events); these occurred twice as often in extreme wet years compared to average years. In contrast, these large precipitation events were rare in extreme dry years. Less important for distinguishing extreme wet from dry years were mean event size and frequency, or the number of dry days between events. However, extreme dry years were distinguished from average years by an increase in the number of dry days between events. These precipitation regime attributes consistently differed between extreme wet and dry years across 12 major terrestrial ecoregions from around the world, from deserts to the tropics. Thus, we recommend that climate change experiments and model simulations incorporate these differences in key precipitation regime attributes, as well as amount into treatments. This will allow experiments to more realistically simulate extreme precipitation years and more accurately assess the ecological consequences.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Univariate and multivariate methods are commonly used to explore the spatial and temporal dynamics of ecological communities, but each has limitations, including oversimplification or ion of ...communities. Rank abundance curves (RACs) potentially integrate these existing methodologies by detailing species‐level community changes. Here, we had three goals: first, to simplify analysis of community dynamics by developing a coordinated set of R functions, and second, to demystify the relationships among univariate, multivariate, and RACs measures, and examine how each is influenced by the community parameters as well as data collection methods. We developed new functions for studying temporal changes and spatial differences in RACs in an update to the R package library(“codyn”), alongside other new functions to calculate univariate and multivariate measures of community dynamics. We also developed a new approach to studying changes in the shape of RAC curves. The R package update presented here increases the accessibility of univariate and multivariate measures of community change over time and difference over space. Next, we use simulated and real data to assess the RAC and multivariate measures that are output from our new functions, studying (1) if they are influenced by species richness and evenness, temporal turnover, and spatial variability and (2) how the measures are related to each other. Lastly, we explore the use of the measures with an example from a long‐term nutrient addition experiment. We find that the RAC and multivariate measures are not sensitive to species richness and evenness and that all the measures detail unique aspects of temporal change or spatial differences. We also find that species reordering is the strongest correlate of a multivariate measure of compositional change and explains most community change observed in long‐term nutrient addition experiment. Overall, we show that species reordering is potentially an understudied determinant of community changes over time or differences between treatments. The functions developed here should enhance the use of RACs to further explore the dynamics of ecological communities.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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