Distribution prediction model for Erioderma mollissimum in Atlantic Canada

Cameron, Robert P.; Neily, Tom; Clayden, Stephen R.

doi:10.1639/0007-2745-114.1.231

Cited by 12 publications

(15 citation statements)

References 10 publications

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“…), ecological and practical criteria, and their effect on small-scale niche conditions governing microclimate suitability (temperature and moisture) noted from similar studies (e.g. Glavich et al 2005;Ellis et al 2007;Cameron et al 2011;Dymytrova et al 2016). All the raster data used in this analysis were first formatted to 50 m resolution to ensure alignment in analysis and as an intermediate resolution between fine and coarse layers.…”

Section: Methodsmentioning

confidence: 99%

Habitat associations and distribution model forFuscopannaria leucostictain Nova Scotia, Canada

Pearson¹,

Cameron

McMullin

2018

The Lichenologist

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Fuscopannaria leucosticta is a rare and understudied cyanolichen with an interesting and unusual distribution in tertiary relict hotspots worldwide. There is a relatively large population in eastern North America, where it occurs mostly throughout the Appalachian Mountains and reaches its northernmost extent in New Brunswick and Nova Scotia, Canada. The ability to detect this species, and thus determine its habitat requirements, is critical for understanding how it might be affected by humaninduced environmental degradation. Maximum entropy modelling with MaxEnt was used to predict the distribution of suitable habitat for this species in Nova Scotia using 62 presence locations, 1405 pseudoabsence locations and four environmental covariates: depth to water table (a proxy for relative soil moisture), distance to the coast and mean annual temperature and precipitation. Our predictive maps identify important habitat features and areas of high suitability in Nova Scotia with an area under the curve value of 0·85. The predicted distribution of this lichen was most affected by temperature. This study elucidates locations as well as species-habitat relationships for F. leucosticta, providing land managers with baseline data that can aid in the discovery of additional populations and provide a better understanding of its ecological requirements which will support the development of sound conservation strategies for this rare lichen.

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Section: Methodsmentioning

confidence: 99%

Habitat associations and distribution model forFuscopannaria leucostictain Nova Scotia, Canada

Pearson¹,

Cameron

McMullin

2018

The Lichenologist

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“…However, bioclimatic modelling of lichens has been used for a wide variety of purposes, and many such studies have explored the relationship of lichen distribution to baseline climate only, alongside a range of other covariables, and without projection to climate change scenarios (Table 1). Thus, lichen bioclimatic modelling has been used to test taxonomic hypotheses [32][33][34][35][36], improve understanding of threatened species [37][38][39][40][41][42][43][44][45] including through conservation design [46,47], identify indicator species [48][49][50][51], or to test and improve the practical application of bioclimatic methods [37,[52][53][54]. These studies at the baseline highlight three key interrelated decisions characterising the development process for any bioclimatic model: 1.…”

Section: Bioclimatic Analysis Of Lichensmentioning

confidence: 99%

“…Lichen distributions are dynamic [55] and any shifts within this baseline period are discounted; there is an important balance to be struck between a time period that extends to provide a reliable distribution, minimising issues of spatial bias [56,57], while constraining this period to represent as stable a distribution as is possible with respect to prevailing climate. Furthermore, field occurrence records provide 'presence-only' data which present an additional statistical challenge that has been handled along a continuum, as follows: (i) by generating a constrained set of pseudo-absences [58,59] for use with standard forms of regression such as generalised linear or additive models [33,39] or with alternative methods that facilitate nested interactions such as classification and regression trees including random forest [33], (ii) using a controlled selection of 'background' pseudo-absence points as applied in MAXENT [34,35,40,[43][44][45], or alternatively (iii) using presence-only statistical methods that compare occurrences to the properties of an entire environmental 'background' [38,46,47]. Lichen bioclimatic models have thus used a rich variety of statistical techniques (Table 1), extending to include nonparametric multiplicative regression that has been applied to >50% of studies with abundance or presence-absence data [36,48,50,51].…”

Section: Bioclimatic Analysis Of Lichensmentioning

confidence: 99%

Climate Change, Bioclimatic Models and the Risk to Lichen Diversity

Ellis

2019

Diversity

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This paper provides an overview of bioclimatic models applied to lichen species, supporting their potential use in this context as indicators of climate change risk. First, it provides a brief summary of climate change risk, pointing to the relevance of lichens as a topic area. Second, it reviews the past use of lichen bioclimatic models, applied for a range of purposes with respect to baseline climate, and the application of data sources, statistical methods, model extents and resolution and choice of predictor variables. Third, it explores additional challenges to the use of lichen bioclimatic models, including: 1. The assumption of climatically controlled lichen distributions, 2. The projection to climate change scenarios, and 3. The issue of nonanalogue climates and model transferability. Fourth, the paper provides a reminder that bioclimatic models estimate change in the extent or range of a species suitable climate space, and that an outcome will be determined by vulnerability responses, including potential for migration, adaptation, and acclimation, within the context of landscape habitat quality. The degree of exposure to climate change, estimated using bioclimatic models, can help to inform an understanding of whether vulnerability responses are sufficient for species resilience. Fifth, the paper draws conclusions based on its overview, highlighting the relevance of bioclimatic models to conservation, support received from observational data, and pointing the way towards mechanistic approaches that align with field-scale climate change experiments.

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“…The distribution and genetic variations of foliicolous lichens have received less attention than other lichen groups, likely due to their small and inconspicuous morphology [3,4]. Various environmental factors, such as climate, elevation, and vegetation are associated with the lichen distribution [5,6,7,8,9]. Therefore, an understanding of the relationship between environmental factors and lichen occurrence is key to identifying and predicting the distribution of lichens [10].…”

Section: Introductionmentioning

confidence: 99%

Distribution of Foliicolous Lichen Strigula and Genetic Structure of S. multiformis on Jeju Island, South Korea

Woo

Hur

2019

Microorganisms

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Strigula is a pantropic foliicolous lichen living on the leaf surfaces of evergreen broadleaf plants. In South Korea, Strigula is the only genus of foliicolous lichen recorded from Jeju Island. Several Strigula species have been recorded, but the ecology of Strigula in South Korea has been largely unexplored. This study examined the distribution and genetic structure of Strigula on Jeju Island. The distribution was surveyed and the influence of environmental factors (e.g., elevation, forest availability, and bioclimate) on the distribution was analyzed using a species distribution modeling analysis. In addition, the genetic variations and differentiation of Strigula multiformis populations were analyzed using two nuclear ribosomal regions. The distribution of Strigula was largely restricted to a small portion of forest on Jeju Island, and the forest availability was the most important factor in the prediction of potential habitats. The genetic diversity and differentiation of the S. multiformis population were found to be high and were divided according to geography. On the other hand, geographic and environmental distance did not explain the population differentiation. Distribution and population genetic analysis suggested that the available habitat and genetic exchange of Strigula on Jeju Island are limited by the lack of available forest in the lowlands.

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Distribution prediction model for Erioderma mollissimum in Atlantic Canada

Cited by 12 publications

References 10 publications

Habitat associations and distribution model forFuscopannaria leucostictain Nova Scotia, Canada

Habitat associations and distribution model forFuscopannaria leucostictain Nova Scotia, Canada

Climate Change, Bioclimatic Models and the Risk to Lichen Diversity

Distribution of Foliicolous Lichen Strigula and Genetic Structure of S. multiformis on Jeju Island, South Korea

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