Spatial, temporal, and life history assumptions influence consistency of landscape effects on species distributions

Tavernia, Brian G.; Reed, J. Michael

doi:10.1007/s10980-010-9482-1

Cited by 8 publications

(6 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other scales shown to be relevant to urban pond ecosystems include 100 m for macroinvertebrates in West Midlands, UK (Thornhill et al. ), 100–300 m for wetland birds in eastern Massachusetts, USA (Tavernia and Reed ), and 500 m for submerged and floating‐leaved macrophytes in Hyogo, Japan (Akasaka et al. ).…”

Section: Impact Of Urbanization On the Biodiversity Of Urban Ponds Anmentioning

confidence: 99%

“…Much larger radii (800 m-1.8 km) were found to influence vegetation and benthic macroinvertebrate assemblages in urban ponds in Ottawa (Patenaude et al 2015). Other scales shown to be relevant to urban pond ecosystems include 100 m for macroinvertebrates in West Midlands, UK (Thornhill et al 2017), 100-300 m for wetland birds in eastern Massachusetts, USA (Tavernia and Reed 2010), and 500 m for submerged and floating-leaved macrophytes in Hyogo, Japan (Akasaka et al 2010). Differences in the scale of influence have also been reported for the same taxonomic group (e.g., amphibians): 200 m in Gresham, Oregon, USA (Guderyahn et al 2016), 300-1000 m in the Eastern and Central USA (Marsh 2017), and 1 km in southeastern Australia (Villasenor et al 2017).…”

Section: Landscape-scale Factorsmentioning

confidence: 99%

See 1 more Smart Citation

Review: Toward management of urban ponds for freshwater biodiversity

2019

View full text Add to dashboard Cite

Many cities around the world are expanding and this trend in urbanization is expected to sharply increase over coming decades. At the same time, the integration of green and blue spaces is widely promoted in urban development, potentially offering numerous benefits for biodiversity. This is particularly relevant for urban waterbodies, a type of ecosystem present in most cities. However, site managers often lack the knowledge base to promote biodiversity in these waterbodies, which are generally created to provide other ecosystem services. To address this, our review presents guidelines for promoting biodiversity in urban ponds. We found a total of 516 publications indexed in ISI Web of Sciences related to this topic, of which 279 were retained for the purposes of our review. The biodiversity of urban ponds, measured by species richness, appears to be generally lower than in rural ponds; however, urban ponds often support threatened species. Furthermore, if well managed, urban ponds have the potential to support a much greater biodiversity than they currently do. Indeed, this review shows that a range of urban factors can impair or promote pond biodiversity, including many that can easily be controlled by site managers. Local factors include design (surface area, pond depth, banks and margins, shade, shoreline irregularity), water quality (conductivity, nutrients, heavy metals), and hydroperiod and biotic characteristics (stands of vegetation, fish, invasive species). Important regional factors include several indicators of urbanization (roads, buildings, density of population, impervious surfaces, car traffic), and the presence of other wetlands or green spaces in the surrounding landscape. We considered each of these factors and their potential impact on freshwater biodiversity. Taking into account the management measures listed in the publications reviewed, we have proposed a framework for the management of urban ponds, with guidelines to promote biodiversity and other ecosystem services, and to avoid ecosystem disservices or the creation of ecological traps. At the city scale, the biodiversity of a pondscape benefits from a high diversity of pond types, differing in their environmental characteristics and management.

show abstract

Section: Impact Of Urbanization On the Biodiversity Of Urban Ponds Anmentioning

confidence: 99%

Section: Landscape-scale Factorsmentioning

confidence: 99%

Review: Toward management of urban ponds for freshwater biodiversity

2019

View full text Add to dashboard Cite

show abstract

“…During the past three decades, there were many attempts to elucidate the relationships between spatial pattern, as captured in landscape metrics, and species distributions, with varying levels of success. Recent examples include characterizing the occurrence of avian species (Cushman and McGarigal 2004, Zuckerberg and Porter 2010), avian species richness (Tavernia and Reed 2010), plant community composition (Goslee and Sanderson 2010), and the occurrence of large mammals (Gaucherel et al 2010).…”

mentioning

confidence: 99%

Complex effects of scale on the relationships of landscape pattern versus avian species richness and community structure in a woodland savanna mosaic

Bar‐Massada¹,

Wood²,

Pidgeon³

et al. 2011

Ecography

View full text Add to dashboard Cite

Landscape pattern metrics are widely used for predicting habitat and species diversity. However, the relationship between landscape pattern and species diversity is typically measured at a single spatial scale, even though both landscape pattern, and species occurrence and community composition are scale‐dependent. While the effects of scale on landscape pattern are well documented, the effects of scale on the relationships between spatial pattern and species richness and composition are not well known. Here, our main goal was to quantify the effects of cartographic scale (spatial resolution and extent) on the relationships between spatial pattern and avian richness and community structure in a mosaic of grassland, woodland, and savanna in central Wisconsin. Our secondary goal was to evaluate the effectiveness of a newly developed tool for spatial pattern analysis, multiscale contextual spatial pattern analysis (MCSPA), compared to existing landscape metrics. Landscape metrics and avian species richness had quadratic, exponential, or logarithmic relationships, and these patterns were generally consistent across two spatial resolutions and six spatial extents. However, the magnitude of the relationships was affected by both resolution and extent. At the finer resolution (10‐m), edge density was consistently the best predictor of species richness, followed by an MCSPA metric that measures the standard deviation of woody cover across extents. At the coarser resolution (30‐m), NDVI was the best predictor of species richness by far, regardless of spatial extent. Another MCSPA metric that denotes the average woody cover across extents, together with percent of woody cover, were always the best predictors of variation in avian community structure. Spatial resolution and extent had varying effects on the relationships between spatial pattern and avian community structure. We therefore conclude that cartographic scale not only affects measures of landscape pattern per se, but also the relationships among spatial pattern, species richness, and community structure, often in complex ways, which reduces the efficacy of landscape metrics for predicting the richness and diversity of organisms.

show abstract

“…As ecologists continue to investigate reasons for interannual variation in animal–landscape relationships (Gutzwiller & Barrow, ; Riffell & Gutzwiller, ; Campbell et al ., ; Tavernia & Reed, ), it will be necessary to develop measures of interannual variation that can be used to compare interannual variability across different species and different studies. We found that the specific variables included in regression models were influenced by different MSC, even though predictive ability of those different models may have been quite similar (see also Murtaugh, ).…”

Section: Discussionmentioning

confidence: 99%

“…regional precipitation) scales. Recently, interannual variation in modelled relationships has been found to be weakly related to species' traits (Riffell & Gutzwiller, ; Tavernia & Reed, ) but strongly related to model selection uncertainty (the likelihood that the selected bird–landscape model is the best; Riffell & Gutzwiller, ). This latter result indicates that interannual variation among modelled relationships may be caused in part by methodological factors related to modelling, sampling or both (Riffell & Gutzwiller, ).…”

Section: Introductionmentioning

confidence: 99%

Model selection criteria affect measures of temporal variation in animal–landscape regression models

Riffell

Gutzwiller

2012

Diversity and Distributions

View full text Add to dashboard Cite

Aim Temporal variation in animal-landscape relationships hinders management decisions and development of conservation policy. Animal-landscape relationships may be modelled using several model selection criteria (MSC), but how such criteria affect measures of temporal variation of modelled relationships is unclear.Location Appalachian Mountains, USA.Methods We used results from linear and logistic regression models for 98 bird species over a 5-year period to compute two measures of interannual variation in bird-landscape relationships: model consistency reflected the degree to which variables and directions of relationships in a model were the same. R 2 coefficient of variation (R 2 CV) reflected the degree to which the strength of relationships was the same. We calculated both metrics for each species' models after using four common MSC: AIC c , BIC, stepwise-0.15 and adjusted R 2 .Results Although positively correlated (r = 0.43À> 0.99; P = 0.030À< 0.001), interannual variation metrics for species differed up to 80% among different MSC. One exception was that using stepwise-0.15 and AIC c resulted in identical bird-landscape models > 90% of the time and thus produced the same interannual variation metrics for those models. MSC affected R 2 CV less than model consistency. Model consistency did not differ significantly among MSC. Use of BIC led to the highest R 2 CV (greatest interannual variation), and use of AIC c and stepwise-0.15 led to the lowest R 2 CV.Main conclusions Comparisons of interannual variation metrics across studies or regions should be based on models derived from the same MSC. When cross-study comparisons must be made, effects of MSC on interannual variation in the strength of relationships (R 2 CV) may be smaller than effects of MSC on interannual variation in the identity of relationships (model consistency). Model consistency metrics could potentially be used in concert with other model evaluation criteria to refine landscape-based species conservation planning.

show abstract

Spatial, temporal, and life history assumptions influence consistency of landscape effects on species distributions

Cited by 8 publications

References 43 publications

Review: Toward management of urban ponds for freshwater biodiversity

Review: Toward management of urban ponds for freshwater biodiversity

Complex effects of scale on the relationships of landscape pattern versus avian species richness and community structure in a woodland savanna mosaic

Model selection criteria affect measures of temporal variation in animal–landscape regression models

Contact Info

Product

Resources

About