Essential oil variation within and among natural populations of Lavandula latifolia and its relation to their ecological areas

Essential oil yield and composition in seven natural populations of Lavandula latifolia from the eastern Iberian Peninsula were determined by GC/MS. Twenty-eight constituents were identified, accounting for 92.0–95.4% of the total oils. These oils were dominated by the monoterpene fraction and three of them (linalool, cineole and camphor) constituted 79.5–86.9% of the oil from flowers. Essential oil yield in leaves and flowers varied among and within populations, but hierarchic analyses of variance showed that the proportion of variation attributable to individuals was significantly higher than that attributable to population differences. Principal component and cluster analyses allowed three groups of flower essential oils to be distinguished according to their high, intermediate and low proportion of linalool. These essential oil types are respectively correlated to the Supra-, Meso- and Thermo-Mediterranean bioclimatic belts where the populations are located. A genetic analysis based on those terpenes that showed a trimodal distribution roughly corroborated the relationships between the seven populations obtained from the ordination analyses and emphasizes the distinctiveness of some of the populations.     

The role of land cover in bioclimatic models depends on spatial resolution

Aim We explored the importance of climate and land cover in bird species distribution models on multiple spatial scales. In particular, we tested whether the integration of land cover data improves the performance of pure bioclimatic models. Location Finland, northern Europe. Methods The data of the bird atlas survey carried out in 1986–89 using a 10 × 10 km uniform grid system in Finland were employed in the analyses. Land cover and climatic variables were compiled using the same grid system. The dependent and explanatory variables were resampled to 20‐km, 40‐km and 80‐km resolutions. Generalized additive models (GAM) were constructed for each of the 88 land bird species studied in order to estimate the probability of occurrence as a function of (1) climate and (2) climate and land cover variables. Model accuracy was measured by a cross‐validation approach using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Results In general, the accuracies of the 88 bird–climate models were good at all studied resolutions. However, the inclusion of land cover increased the performance of 79 and 78 of the 88 bioclimatic models at 10‐km and 20‐km resolutions, respectively. There was no significant improvement at the 40‐km resolution. In contrast to the finer resolutions, the inclusion of land cover variables decreased the modelling accuracy at 80km resolution. Main conclusions Our results suggest that the determinants of bird species distributions are hierarchically structured: climatic variables are large‐scale determinants, followed by land cover at finer resolutions. The majority of the land bird species in Finland are rather clearly correlated with climate, and bioclimate envelope models can provide useful tools for identifying the relationships between these species and the environment at resolutions ranging from 10 km to 80 km. However, the notable contribution of land cover to the accuracy of bioclimatic models at 10–20‐km resolutions indicates that the integration of climate and land cover information can improve our understanding and model predictions of biogeographical patterns under global change.     

Ancient woodland indicators signal the climate change risk for dispersal-limited species

The climate change risk to biodiversity operates alongside a range of anthropogenic pressures. These include habitat loss and fragmentation, which may prevent species from migrating between isolated habitat patches in order to track their suitable climate space. Predictive modelling has advanced in scope and complexity to integrate: (i) projected shifts in climate suitability, with (ii) spatial patterns of landscape habitat quality and rates of dispersal. This improved ecological realism is suited to data-rich model species, though its broader generalisation comes with accumulated uncertainties, e.g. incomplete knowledge of species response to variable habitat quality, parameterisation of dispersal kernels etc. This study adopts ancient woodland indicator species (lichen epiphytes) as a guild that couples relative simplicity with biological rigour. Subjectively-assigned indicator species were statistically tested against a binary habitat map of woodlands of known continuity (>250 yr), and bioclimatic models were used to demonstrate trends in their increased/decreased environmental suitability under conditions of ‘no dispersal’. Given the expectation of rapid climate change on ecological time-scales, no dispersal for ancient woodland indicators becomes a plausible assumption. The risk to ancient woodland indicators is spatially structured (greater in a relative continental compared to an oceanic climatic zone), though regional differences are weakened by significant variation (within regions) in woodland extent. As a corollary, ancient woodland indicators that are sensitive to projected climate change scenarios may be excellent targets for monitoring climate change impacts for biodiversity at a site-scale, including the outcome of strategic habitat management (climate change adaptation) designed to offset risk for dispersal-limited species.     

The influence of climate on the distribution of lichens: a case study in a borderline area (Liguria,NW Italy)

The purposes of this article are to quantify the relationship between epiphytic lichen distribution and macroclimatic variables in the study area and to provide a case study for evaluating the predictive role of epiphytic lichens as bioclimatic indicators. The study was carried out in the Liguria region (NW-Italy), a small (5432 km²) borderline area, where phytoclimatic features range from the dry Mediterranean to the Alpine in a few kilometers. Epiphytic lichen diversity was sampled using a standardized protocol [Asta et al (2002) In: Nimis et al (eds) Monitoring with lichens: monitoring lichens. Kluwer, Dordrecht]. Abundance of the species in the sampling sites was related to macroclimatic parameters (yearly average temperature and rainfall) and non-parametric multivariate models were calculated to find significative relationships among predictive and response variables. A total of 59 species showed highly significant relation with macroclimatic parameters. Four groups were selected, by means of a cluster analysis, related to four climatic niches (warm-humid, cold-humid, mesothermic-humid, warm-dry). Distributional pattern of the groups in the survey area showed a good correspondence with the bioclimatic units of Liguria region described by Nimis [(2003) Checklist of the Lichens of Italy 3.0. University of Trieste, Dept of Biology. http://www.dbiodbs.univ.trieste.it. Cited 1 Jun 2006]. A significant subset of epiphytic lichen species in the study area have been proved to be efficient bioclimatic indicator and it is supposed to give good results to monitor climatic changes, in a long-term perspective.     

Identifying climatic niche shifts using coarse‐grained occurrence data: a test with non‐native freshwater fish

Aim We tested whether coarse‐grained occurrence data can be used to detect climatic niche shifts between native and non‐native ranges for a set of widely introduced freshwater fishes. Location World‐wide. Methods We used a global database of freshwater fish occurrences at the river basin scale to identify native and non‐native ranges for 18 of the most widely introduced fish species. We also examined climatic conditions within each river basin using fine‐grained climate data. We combined this information to test whether climatic niche shifts have occurred between native and non‐native ranges. We defined climatic niche shifts as instances where the ranges of a climatic variable within native and non‐native basins exhibit zero overlap. Results We detected at least one climatic niche shift for each of the 18 studied species. However, we did not detect common patterns in the thermal preference or biogeographic origin of the non‐native fish, hence suggesting a species‐specific response. Main conclusions Coarse‐grained occurrence data can be used to detect climatic niche shifts. They also enable the identification of the species experiencing niche shifts, although the mechanisms responsible for these shifts (e.g. local adaptation, dispersal limitation or physiological constraints) have yet to be determined. Furthermore, the coarse‐grained approach, which highlights regions where climatic niche shifts have occurred, can be used to select specific river basins for more detailed, fine‐grained studies.     

Do we need land‐cover data to model species distributions in Europe?

Aim To assess the influence of land cover and climate on species distributions across Europe. To quantify the importance of land cover to describe and predict species distributions after using climate as the main driver. Location The study area is Europe. Methods (1) A multivariate analysis was applied to describe land‐cover distribution across Europe and assess if the land cover is determined by climate at large spatial scales. (2) To evaluate the importance of land cover to predict species distributions, we implemented a spatially explicit iterative procedure to predict species distributions of plants (2603 species), mammals (186 species), breeding birds (440 species), amphibian and reptiles (143 species). First, we ran bioclimatic models using stepwise generalized additive models using bioclimatic variables. Secondly, we carried out a regression of land cover (LC) variables against residuals from the bioclimatic models to select the most relevant LC variables. Finally, we produced mixed models including climatic variables and those LC variables selected as decreasing the residual of bioclimatic models. Then we compared the explanatory and predictive power of the pure bioclimatic against the mixed model. Results (1) At the European coarse resolution, land cover is mainly driven by climate. Two bioclimatic axes representing a gradient of temperature and a gradient of precipitation explained most variation of land‐cover distribution. (2) The inclusion of land cover improved significantly the explanatory power of bioclimatic models and the most relevant variables across groups were those not explained or poorly explained by climate. However, the predictive power of bioclimatic model was not improved by the inclusion of LC variables in the iterative model selection process. Main conclusion Climate is the major driver of both species and land‐cover distributions over Europe. Yet, LC variables that are not explained or weakly associated with climate (inland water, sea or arable land) are interesting to describe particular mammal, bird and tree distributions. However, the addition of LC variables to pure bioclimatic models does not improve their predictive accuracy.     

The Acclimatization Thermal Strain Index (ATSI): a preliminary study of the methodology applied to climatic conditions of the Russian Far East

Travel to distant places where the climate is different to that at home involves a period of short-term acclimatization adjustment following arrival during which the traveler might experience thermally-induced physiological strain. This may be expressed as an “acclimatization thermal loading” (ATL). The first signs of this show up in the respiratory organs. In the current study, the Acclimatization Thermal Strain Index (ATSI) is developed and used for assessment of ATL for recreational travel over a range of climatic conditions. ATSI estimates the impact of short-term acclimatization calculated as the ratio of a difference between respiratory heat losses at the traveler’s home location to respiratory heat losses at the trip destination upon first arriving there. The Russian Far East region is used as a case study. The research focuses on the effects of travel from two locations in the study region. The results show that ATSI values can be significantly different when considering places of trip origin. For example, travel from Anadyr to other locations within the Russian Far East could lead to large ATSI in summer. In contrast, ATSI values are small for travel almost anywhere in the region during winter, but this is against a backdrop of extreme cold for the region as a whole. Here, the diversity of climatic conditions of both heat and cold means short-term adjustment to conditions could be stressful or worse for those who travel to participate in outdoor activities.     

A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area

To investigate the comparative abilities of six different bioclimatic models in an independent area, utilizing the distribution of eight different species available at a global scale and in Australia. Global scale and Australia. We tested a variety of bioclimatic models for eight different plant species employing five discriminatory correlative species distribution models (SDMs) including Generalized Linear Model (GLM), MaxEnt, Random Forest (RF), Boosted Regression Tree (BRT), Bioclim, together with CLIMEX (CL) as a mechanistic niche model. These models were fitted using a training dataset of available global data, but with the exclusion of Australian locations. The capabilities of these techniques in projecting suitable climate, based on independent records for these species in Australia, were compared. Thus, Australia is not used to calibrate the models and therefore it is as an independent area regarding geographic locations. To assess and compare performance, we utilized the area under the receiver operating characteristic (ROC) curves (AUC), true skill statistic (TSS), and fractional predicted areas for all SDMs. In addition, we assessed satisfactory agreements between the outputs of the six different bioclimatic models, for all eight species in Australia. The modeling method impacted on potential distribution predictions under current climate. However, the utilization of sensitivity and the fractional predicted areas showed that GLM, MaxEnt, Bioclim, and CL had the highest sensitivity for Australian climate conditions. Bioclim calculated the highest fractional predicted area of an independent area, while RF and BRT were poor. For many applications, it is difficult to decide which bioclimatic model to use. This research shows that variable results are obtained using different SDMs in an independent area. This research also shows that the SDMs produce different results for different species; for example, Bioclim may not be good for one species but works better for other species. Also, when projecting a “large” number of species into novel environments or in an independent area, the selection of the “best” model/technique is often less reliable than an ensemble modeling approach. In addition, it is vital to understand the accuracy of SDMs' predictions. Further, while TSS, together with fractional predicted areas, are appropriate tools for the measurement of accuracy between model results, particularly when undertaking projections on an independent area, AUC has been proved not to be. Our study highlights that each one of these models (CL, Bioclim, GLM, MaxEnt, BRT, and RF) provides slightly different results on projections and that it may be safer to use an ensemble of models.     

Modelling the effects of climate change on the potential feeding activity of Thaumetopoea pityocampa (Den. & Schiff.) (Lep., Notodontidae) in France

Aim We investigated whether climate change has affected the potential feeding activity of a winter active larva, the pine processionary moth (PPM), Thaumetopoea pityocampa L., and whether it may explain its range expansion. Location The study area is France and, at a smaller scale, the Paris Basin. Methods We used a statistical model derived from Huchon and Démolin [1970 Revue Forestière Française (special issue: La lutte biologique en forêt), 220–234] to test whether their model, updated with climate change, could explain the observed range expansion. Since Battisti and colleagues have recently shown that climate could affect survival of the PPM through its effect on feeding activity, we also developed a mechanistic model based on larval feeding requirements (night air temperature above 0 °C and temperature inside the nest above 9 °C on the preceding day). We reconstructed the geographical distribution of feeding activity and we compared the resulting change with the PPM range expansion. Results The statistical model did not successfully predict the observed expansion but the mechanistic model showed considerable change in the feeding activity of the PPM. In the Paris Basin, the PPM border coincided with a zone unfavourable for feeding activity in the period 1992–96. Feeding conditions became more favourable in the period 2001–04, and the PPM succeeded in crossing this zone. Over larger temporal and spatial scales improved feeding conditions in the north‐western part of France were forecast by the mechanistic model. Main conclusions (1) The range distribution of the PPM in the Paris Basin is no longer limited by unfavourable feeding conditions. (2) The pattern of range expansion of the PPM is now governed mainly by its dispersal capabilities and host tree distribution. (3) At the country scale, this approach gives an approximate prediction of the potential distribution of the PPM, though the model may not be reliable in mountainous regions.