Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution

Meineri, Éric; Dahlberg, C. Johan; Hylander, Kristoffer

doi:10.1016/j.ecolmodel.2015.06.028

Cited by 24 publications

(34 citation statements)

References 49 publications

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“…In agreement with earlier studies, we detected clear evidence of cold air pooling in terms of a positive relation between relative elevation and minimum temperatures Meineri et al, 2015). This micro-meteorological phenomenon may have important implications for forest floor biota, since it determines the occurrence and depth of ground frost at the beginning and end of the growing season (Inouye, 2000).…”

Section: Quantifying Forest Microclimate Drivers Across the Seasonssupporting

confidence: 79%

“…The adiabatic decrease in temperature as a result of the atmospheric thermal stratification is known to differ depending on region, season and daytime (Dingman et al, 2013;Fridley, 2009;Vercauteren et al, 2012). Elevation has often been reported to be a main microclimate driver in areas with complex terrain (Frey et al, 2016;Meineri et al, 2015;Vanwalleghem and Meentemeyer, 2009), whereas in our study it affected only maximum temperatures substantially and only in the cold season, where environmental lapse rates were higher and recognizable even in an area outside steep topographic gradients (see also maps in Fig. 6).…”

Section: Quantifying Forest Microclimate Drivers Across the Seasonscontrasting

confidence: 43%

“…Relative importance of predictors was expressed as sequential R 2 contributions (calculated by dividing the sequential sums of squares by the total response sum of squares) using simple unweighted averages over orderings (R package relaimpo, Grömping, 2006). Model coefficients (estimate and standard error) were reported as averages over ten cross-validations and further translated into effect sizes by multiplying them with the range of the respective predictor Latimer and Zuckerberg, 2016;Meineri et al, 2015). To test the reliability and quality of remotely sensed products, we compared fieldbased canopy cover and basal area with remotely sensed basal area using the Spearman's correlation coefficient.…”

Section: Temperature Modelling Proceduresmentioning

confidence: 99%

“…Especially at higher latitudes and in mixed forests canopy cover, solar angle and their combined effects with topography undergo strong seasonal changes (Lenoir et al, 2013). Temperature extremes during different seasons are limiting for some organisms, when their physiological tolerances are exceeded (Ashcroft et al, 2011;Meineri et al, 2015;Walther et al, 2009;Zimmermann et al, 2009). Additionally, locations with unusual climates (at the extreme ends of temperature gradients) may play a crucial role as future climate refugia .…”

Section: Introductionmentioning

confidence: 99%

“…While many studies have modelled minimum and maximum temperatures (e.g. Fridley, 2009;Geiger et al, 2012;Lookingbill and Urban, 2003;Meineri et al, 2015), only few have considered also seasonal changes in climate-forcing factors and their relative influence on near-ground temperatures (e.g. Ashcroft and Gollan, 2013a).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Monthly microclimate models in a managed boreal forest landscape

Greiser

Meineri

Luoto

et al. 2018

Agricultural and Forest Meteorology

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. Monthly microclimate models in a managed boreal forest landscape. Agricultural and Forest Meteorology, Elsevier Masson, 2018, 250-251, pp.147 -158. 10.1016/j.agrformet.2017 A B S T R A C TThe majority of microclimate studies have been done in topographically complex landscapes to quantify and predict how near-ground temperatures vary as a function of terrain properties. However, in forests understory temperatures can be strongly influenced also by vegetation. We quantified the relative influence of vegetation features and physiography (topography and moisture-related variables) on understory temperatures in managed boreal forests in central Sweden. We used a multivariate regression approach to relate near-ground temperature of 203 loggers over the snow-free seasons in an area of ∼16,000 km 2 to remotely sensed and on-site measured variables of forest structure and physiography. We produced climate grids of monthly minimum and maximum temperatures at 25 m resolution by using only remotely sensed and mapped predictors. The quality and predictions of the models containing only remotely sensed predictors (MAP models) were compared with the models containing also on-site measured predictors (OS models). Our data suggest that during the warm season, where landscape microclimate variability is largest, canopy cover and basal area were the most important microclimatic drivers for both minimum and maximum temperatures, while physiographic drivers (mainly elevation) dominated maximum temperatures during autumn and early winter. The MAP models were able to reproduce findings from the OS models but tended to underestimate high and overestimate low temperatures. Including important microclimatic drivers, particularly soil moisture, that are yet lacking in a mapped form should improve the microclimate maps. Because of the dynamic nature of managed forests, continuous updates of mapped forest structure parameters are needed to accurately predict temperatures. Our results suggest that forest management (e.g. stand size, structure and composition) and conservation may play a key role in amplifying or impeding the effects of climate-forcing factors on near-ground temperature and may locally modify the impact of global warming.

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Section: Quantifying Forest Microclimate Drivers Across the Seasonssupporting

confidence: 79%

Section: Quantifying Forest Microclimate Drivers Across the Seasonscontrasting

confidence: 43%

Section: Temperature Modelling Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Monthly microclimate models in a managed boreal forest landscape

Greiser

Meineri

Luoto

et al. 2018

Agricultural and Forest Meteorology

Self Cite

105

121

View full text Add to dashboard Cite

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Applications of hybrid dynamic Bayesian networks to water reservoir management

et al. 2016

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Bayesian networks (BNs) have been widely applied in environmental modelling to predict the behavior of an ecosystem under conditions of change. However, this approximation doesn't take time into consideration. To solve this issue, an extension of BNs, the dynamic Bayesian networks (DBNs), has been developed in mathematics and computer science areas but has scarcely been applied in environmental modelling. This paper presents the application of DBN to water reservoir systems in Andalusia, Spain. The aim is to predict changes in the percent fullness of the reservoirs under the irregular rainfall patterns of Mediterranean watersheds. In comparison to static BNs, DBNs provide results that can be extrapolated to a particular time so that a climate change scenario can be studied in detail over time. Because results are expressed by density functions rather than unique values, several metrics are obtained from the results, including the probability of certain values. This allows the probability that water level in a reservoir reaches a certain level to be directly computed.

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Revealing topoclimatic heterogeneity using meteorological station data

Aalto

Riihimäki

Meineri

et al. 2017

Intl Journal of Climatology

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Climate is a crucial driver of the distributions and activity of multiple biotic and abiotic processes, and thus high‐quality and high–resolution climate data are often prerequisite in various environmental research. However, contemporary gridded climate products suffer critical problems mainly related to sub‐optimal pixel size and lack of local topography‐driven temperature heterogeneity. Here, by integrating meteorological station data, high‐quality terrain information and multivariate modelling, we aim to explicitly demonstrate this deficiency. Monthly average temperatures (1981–2010) from Finland, Sweden and Norway were modelled using generalized additive modelling under (1) a conventional (i.e. considering geographical location, elevation and water cover) and (2) a topoclimatic framework (i.e. also accounting for solar radiation and cold‐air pooling). The performance of the topoclimatic model was significantly higher than the conventional approach for most months, with bootstrapped mean R2 for the topoclimatic model varying from 0.88 (January) to 0.95 (October). The estimated effect of solar radiation was evident during summer, while cold air pooling was identified to improve local temperature estimates in winter. The topoclimatic modelling exposed a substantial temperature heterogeneity within coarser landscape units (>5 °C/1 km−2 in summer) thus unveiling a wide range of potential microclimatic conditions neglected by the conventional approach. Moreover, the topoclimatic model predictions revealed a pronounced asymmetry in average temperature conditions, causing isotherms during summer to differ several hundreds of metres in altitude between the equator and pole facing slopes. In contrast, cold‐air pooling in sheltered landscapes lowered the winter temperatures ca. 1.1 °C/100 m towards the local minimum altitude. Noteworthy, the analysis implies that conventional models produce biassed predictions of long‐term average temperature conditions, with errors likely to be high at sites associated with complex topography.

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Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution

Cited by 24 publications

References 49 publications

Monthly microclimate models in a managed boreal forest landscape

Monthly microclimate models in a managed boreal forest landscape

Applications of hybrid dynamic Bayesian networks to water reservoir management

Revealing topoclimatic heterogeneity using meteorological station data

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