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opencc-by-4.0Nov 2024View details →

Data from: "Cross-realm transferability of species distribution models – species characteristics and prevalence matter more than modelling methods applied"

<h2>Abstract</h2> This data contains occurrence observations (presence-absence) of 11 aquatic macrophytes from Bothnian Sea and Lake Puruvesi, and environmental covariates used to build species distribution models (SDMs) in  paper "Cross-realm transferability of species distribution models – species characteristics and prevalence matter more than modelling methods applied" in Ecological Modelling. In addition to data files, also R code for fitting the SDMs is supplied, as is the R code to replicate the analysis conducted in the paper. The data is stored in rdata format (point data), without coordinate information due to data policy restrictions. The species in the data are Isoëtes lacustris, Isoëtes echinospora, Ranunculus reptans, Ranunculus schmalhausenii, Potamogeton berchtoldii, Potamogeton perfoliatus, Potamogeton gramineus, Myriophyllum alterniflorum, Equisetum fluviatile, Eleocharis acicularis and Elodea canadensis. The environmental covariates are bottom water salinity, turbidity, sandy substrate occurrence, colored dissolved organic matter (CDOM), surface fetch, sampling depth, total nitrogen and total phosphorus, and distance to closest Phragmites australis reed bed.  <h2>Objective of the study </h2> The modelling objective of the paper was species distribution model (SDM) transferability assesment. Transferability was assessed using models built in marine areas in projecting the distributions of the target species in Lake Puruvesi, Saimaa, Eastern Finland. Macrophyte mapping data from Lake Puruvesi was used as independent test data, against which transferability of the models was assessed. <h2>Location</h2> The species data was collected from two geographic areas: Bothnian Bay (Baltic Sea) and Lake Puruvesi (Eastern Finland). The marine observations from Bothnian Bay were split into three overlapping areas (areas 1-3), to test the effect of input data gradient length to SDM transferability. The largest marine sampling area (Area 3) ranged from 62.95, 65.91 latitude and 19.14, 27.99 longitude. Area 2 ranged from 63.95, 65.91 latitude and 21.53, 27.99 longitude. Area 1 ranged from 64.91, 65.91 latitude and 23.82, 27.99 longitude. The Hummonselkä subbasin of Lake Puruvesi, where the macrophyte test data was collected, is located at 61.89, 62.05 latitude and 29.58, 29.78 longitude. <h2>Species data </h2> The species observations were collected using diving transects placed in the floor of the sea or lake, and species observations were recorded in 2 m22 grid cells separated by 10 meters along the transect or 1 meters depth, depending which criteria was met first. The species data was collected in 2010 - 2020 from marine area, and 2017 from Lake Puruvesi. All macrophytes in 2 x 1 m frames were identified to species level by the diver, and the data contained information on species presence or absence in each grid cell. The diving transects were conducted using systematic survey protocol used in the underwater inventories of the Finnish Underwater Biodiversity Survey Program (VELMU) (Frosblom & Virtanen et al. 2024). The locations of the diving transects were not randomly distributed, but were placed using expert judgement. As all our study species are macroscopic and relatively easily identifiable in the field (with the exception of possibility of mixing I. echinospora and I. lacustris), we consider the absences in our observation data to indicate true absences. That said, as the observation area is rather small (2 m2), it is possible that a species may be found in the site of investigation (e.g. a small lagoon) but be located outside the vegetation sampling grid. <h2>Environmental data</h2> <h3>Bottom water salinity</h3> The seasonal mean bottom water salinity was modeled using a generalized additive model with mean salinity as response, with log-link and gamma distribution for the errors. This was necessary to keep the resulting predictions positive. Bottom depth, CDOM, river influence and spatial location were used as predictors. Data from 448 locations were used and each location had a minimum of three observations. The model was validated using 30 % of the data left outside of the model fitting. The explained deviance of the model was 0.94 and the correlation between raw data and predicted values was 0.95 with few outliers. <h3>Turbidity</h3> Maps of turbidity (in FNU, Formazin Nephelometric Unit) were generated from Sentinel-2 Multi-Spectral Imager (MSI) observations using the Case-2 Regional Coast Colour (C2RCC) bio-optical inversion model, containing separate atmospheric correction and water quality parts. Before computing C2RCC, the original 10-meter input data was downsampled to 60 meters. The output variable of the C2RCC processor correlative to turbidity is the backscattering of total suspended sediments at 443 nm, which was further calibrated into turbidity (FNU) values using SYKE's empirical equations for coastal waters and clear lakes (for a similar approach, see Attila et al. (2013) and Sagerman, Hansen, and Wikström (2020)). Monthly observations of turbidity were aggregated into median composites to reduce the effects of cloud cover and other disturbances. Due to low solar elevation and ice cover in winter, the turbidity distribution maps are generated only for the summer months (May to September). The current processing covers years 2017 to 2021. An average raster layer was created from monthly observations as input for SDM building. <h3>Probability of sandy substrate </h3> Random forest model was used to classify sandy bottoms from Sentinel 2 MSI satellite images in shallow water areas. Identifying sandy substrate is based on the higher reflectance compared to other substrates. The model was trained and validated using diver recorded field observations in the Baltic area, and diver recorded and echo sounding observations in the freshwater area. For full coverage including areas beyond the shallow water, the satellite image classification was combined with boosted regression tree modelling result in the Baltic, and echo sounding based product in the freshwater region. The resulting layers were probabilities of sandy substrate with 10-meter cell resolution. <h3>CDOM</h3> We used different methods to estimate the CDOM levels in Bothnian Bay and Lake Puruvesi, based on biogeochemical model data and satellite images. For Lake Puruvesi, we applied the Finnish Environment Institute's (Syke) in-house CDOM algorithm to the Sentinel-2 MSI images processed by the C2RCC bio-optical processor (Brockmann et al. 2016). The observations in 10 m resolution were aggregated as monthly averages for each month of the summer season (May to October) from 2017 to 2021. For Bothnian Bay, we used Syke's in-house Sentinel-2 MSI CDOM layers (resolution: 60 m) aggregated as seasonal averages (1 Jul to 7 Sep). CDOM values are given as absorption coefficient of CDOM at 400 nm [m⁻¹]. <h3>Surface fetch</h3> A surface fetch raster was produced to the Puruvesi and Bothnian Bay. The analysis required a feature layer of shorelines from Puruvesi and Baltic Sea. First, we created polyline from north to south spanning over the whole area of interest with a gap of 20 meters which is also the resolution of the output raster. These lines were then cut each time they hit the shoreline and the part of the line that was overlapping land was removed. The distance of the remaining lines was then calculated and a point with the distance value was created every 20 meters. Each time the line was cut when hitting an island for example and starting again from the other side of the island, the distance calculation started from 0. This created a point dataset with a distance value in each point. We repeated the procedure for 15 times for different compass directions with 22.5 degree intervals and calculated average fetch for each point location on 20 meters grid from these 15 point layers. <h3>Depth</h3> Depth was measured by a diver using a dive computer while surveying each vegetation grid cell, and measured depth was used when projecting model results to Puruvesi (transferability performance). In addition, a depth model for the freshwater region was created from Sentinel 2 MSI satellite image using the logarithmic band ratio model of blue and red band. The model was calibrated using diver recorded field observations and validated against echo sounding measurements. For more complete coverage and to include deep areas, echo soundings from multiple sources were combined with the satellite derived bathymetry. The cell resolution of the resulting depth layer was 10 meters. <h3>Total nitrogen and phosphorus</h3> Mean total nitrogen and phosphorus layers for marine area were produced using ArcGIS "splines with barriers" tool for the EEZ of Finland with 20 meters spatial resolution (Virtanen et al. 2018). Summer (July - September) nutrient measurements from 0 to 10 meters depth between 2010 and 2020, obtained from the VESLA database, were used as input data for the interpolation. Nitrogen and phosphorus measurements in Puruvesi between 2010 and 2020 was gathered from the VESLA database. Data from July to September was selected to represent the growing season. A mean value of NTOT and PTOT was then calculated for each location. Spline with Barriers (SwB) tool was used to interpolate the values (Arcmap 10.7.1). The tool uses a feature layer as barrier to create the raster representing only the area of interest. For the barrier and the extent of the interpolated raster we used a shapefile representing Lake Puruvesi shoreline. The resolution was set to 5x5 meters. SwB tool created an "extent box" around the area of interest which was removed with Extract by Mask tool using the shoreline feature layer. After the interpolation we noticed that either one of the locations was situated on land or the polygon used as barrier was "leaking". SwB doesn´t interpolate areas that doesn't have locations with values or aren´t connected to the main body of water. To fix this, the raster was extended outwards based on the values of nearby cells and after that the raster was masked again to remove any cells on land. The phosphorus interpolation provided negative values in southern parts on Enanlahti in Kontiolahti and Muholanlahti. These negative values were caused by considerably larger phosphorus values in Enanlahti Lamminniemi (9m) Enanlahti Lamminniemi (4m) locations when compared with the nearby Puruvesi Enanlahti location. The interpolation apparently continued to decrease the values according to the trend set by the difference between these locations and caused it to reach negative values. The southern parts of the bay, about 750 meters, was removed and new values were calculated based on the surrounding cells with Focal Statistics tool. The interpolations were validated by removing 20 % of the locations and reproducing the interpolation. The removed locations and their values were then compared to the interpolated raster. R&lowast;2&lowast;2 value from phosphorus interpolation model was 0.91 after removing two outliers and R22 value from nitrogen interpolation model was 0.715 after removing one outlier. <h3>Distance to closest reed </h3> The aquatic vegetation (Phragmites australis reeds) presence/absence maps were also generated from Sentinel-2 MSI data. The processing included extracting one month of data (July 2019) from green and near-infra-red bands from Sentinel-2 Global Mosaic (S2GM) service and transforming those to normalized-difference vegetation indices (NDVIs). After that, Bayesian statistics were used to predict the posterior probability of vegetation occurrence when distance from shore and NDVI were used as predictor variables. The posterior variable was thresholded and the resulting vegetation presence areas were sieved so that both too small vegetation areas (fewer than 5 pixels) or areas that were not directly attached to shoreline were removed. The resulting map has 10 m pixel size and tentatively represents the locations of reed belts or other shoreline-attached vegetation. This EO-based layer could also be referred to as helophytes or helophytic macrophytes, as it denotes a specific zone of vegetation with emergent aquatic plants containing leaf-green, particularly those that grow densely and have horizontally oriented leaves. In some lakes, this layer can represent, for example, thick stands of Equisetum fluviatile, although in most cases, it is associated with common reed belts. The approach is described in more detail in Koponen et al. (2022). <h2>Data partitioning </h2> Data was partitioned with 70/30 splitting into training and test (interpolation accuracy) data. The splitting was repeated 100 times for each species by randomly selecting 70 % of observations which were used to build each of the SDMs (GLM, GAM, BRT and BART). The partitioning was repeated for each of the three input data areas and 11 species. The input data indexes for replicating the split are supplied in the data files.  <h2>R code </h2> Code files contain scripts for fitting the SDM models described in the paper using the data. Also code for calculating calidation statistics (modelling results) and code for statistical analyses for making inferences in the paper, are supplied.  <h2>Additional info</h2> More details on modelling protocol may be found in the original paper in Ecological Modelling, and in the Supplementary Information file of the original paper, which follows the model reporting template "ODMAP" by Zurell et al. (2020). <h2>References </h2> Attila, J., et al. 2013. MERIS Case II water processor comparison on coastal sites of the northern Baltic Sea. - Remote Sensing of Environment 128: 138-149. Brockmann, C., et al. 2016. Evolution of the C2RCC neural network for Sentinel 2 and 3 for the retrieval of ocean colour products in normal and extreme optically complex waters. - In: Living Planet Symposium. p. 54. Forsblom, L., et al. 2024. Finnish inventory data of underwater marine biodiversity   -Scientic data Koponen, S., et al. 2022. Blue Carbon Habitats: – a comprehensive mapping of Nordic salt marshes for estimating Blue Carbon storage potential. - Nordisk Ministerråd. Sagerman, J., et al. 2020. Effects of boat traffic and mooring infrastructure on aquatic vegetation: A systematic review and meta-analysis. - Ambio 49: 517-530. Zurell, D., et al. 2020. A standard protocol for reporting species distribution models. - Ecography 43: 1261-1277.

opencc-zeroOct 2021View details →

Bushmeat yields, species extinction rates and ecosystem-level impacts of bushmeat harvesting as predicted by the Madingley General Ecosystem Model

The datasets contain data generated using the Madingley General Ecosystem Model for experiments decribed in the paper: "T. Barychka, G.M.Mace and D.W.Purves (2021) The Madingley General Ecosystem Model predicts bushmeat yields, species extinction rates and ecosystem-level impacts of bushmeat harvesting. Oikos." The Madingley General Ecosystem Model was used to generate predictions of bushmeat yields, extinction rates and broader ecosystem impacts for a range of harvesting intensities of duiker-sized endothermic herbivores. Duiker antelope (such as Cephalophus callipygus and Cephalophus dorsalis) are the most heavily hunted species in sub-Saharan Africa, contributing 34%-95% of all bushmeat in the Congo Basin. In the Madingley, the harvested group was described as "Heterotroph – Herbivore – Terrestrial – Mobile – Iteroparous– Endotherm", with adult bodymasses of 13-21 kg and juvenile bodymasses of >100 g. Harvesting period was set at 30 years (n =30). In the first experiment, we used the Madingley model to predict bushmeat yields ("Harvested Biomasses") and extinction rates ("Density") from harvesting duiker-sized herbivores using proportional harvesting strategy, with harvest rates ranging from 0 to 0.90. These were compared to the estimates of bushmeat yields and survival probabilities for two duiker antelope species (Cephalophus callipygus and Cephalophus dorsalis) from conventional single-species Beverton-Holt model. In the second experiment, we used the Madingley model to generate data on the state ("State") of the harvested ecosystem. The datasets contain estimates of biomasses, abundances, adult and juvenile bodymasses, etc. of the harvested duiker-sized herbivores as well as unharvested herbivores, omnivores and carnivores present in the simulated ecosystem. In the paper we focused on abundances; however, other estimates e.g., adult bodymasses can be used to conduct further studies on the effects of harvesting in tropical ecosystems. Main results of the experiments are that: 1) the Madingley model gave estimates for optimal harvesting rate, and extinction rate, that were qualitatively and quantitatively similar to the estimates from conventional single-species Beverton-Holt model; 2) the Madingley model predicted a background local extinction probability for the target species of at least 10%; 3) at medium and high levels of harvesting of duiker-sized herbivores, the Madingley model predicted statistically significant, but moderate, reductions in the densities of the targeted functional group; increases in small-bodied herbivores; decreases in large-bodied carnivores; and minimal ecosystem-level impacts overall.

opencc-zeroNov 2021View details →

Predictor complexity and feature selection affect Maxent model transferability: evidence from global freshwater invasive species

This dataset contains the following: <ol> <li>Occurrence datasets of five global freshwater invasive species (African sharptooth catfish Clarias gariepinus, Mozambique tilapia Oreochromis mossambicus, American bullfrog Lithobates catesbeianus, red swamp crayfish Procambarus clarkii, and Australian redclaw crayfish Cherax quadricarinatus)</li> <li>Background points for presence-only ecological niche modelling (e.g., Maxent)</li> <li>Example R script (with annotations inline) to conduct model tuning and transferability assessments using Maxent</li> </ol>

opencc-zeroDec 2021View details →

Evidence for niche conservatism in alpine beetles under a climate-driven species pump model

Aim Past glacial climate cycles have generated lineage diversity in alpine habitats, acting as a climate-driven species pump. It is not clear how much this process contributes to ecological diversification of alpine species. To examine this problem, we test patterns of genetic and phenotypic divergence in two co-distributed species complexes of flightless alpine ground beetles. Greater differentiation in ecologically-important functional traits would indicate that ecological selection is an outcome of oscillating climate change, whereas greater differentiation in non-ecological traits would indicate niche conservatism. Location The Cascades Range and Trinity Mountains of western North America. Taxon Members of the Nebria paradisi and N. vandykei species complexes (Insecta: Coleoptera: Carabidae: Nebriinae) Methods We generated genome-wide single nucleotide polymorphism data and mitochondrial sequence data, as well as morphological and physiological data, to compare populations spanning the range of both species. Phylogenetic and population genetic analyses were used to infer the relationships among taxa and populations within each species complex, as well as historical population demography. Support vector machines were used to test for classification of taxa and populations based on ecomorphological, ecophysiological, and male reproductive traits. Mantel tests were then used to assess statistical associations between phenotypic and genetic divergence among populations. Results The N. vandykei and N. paradisi species complexes are each comprised of genetically distinctive populations exhibiting long-term demographic declines. Each phylogeny supports multiple monophyletic groups with geographical cohesion. By examining phenotypic traits among populations in both species' complexes, we show that reproductive trait divergence can discriminate species and population status more effectively than ecomorphological or ecophysiological traits. Reproductive and genetic divergence are significantly correlated in the N. vandykei species complex. Main Conclusions We found limited evidence of ecological selection acting on functional traits. Instead, reproductive and genetic divergence evolved among isolated populations in both species complexes, suggesting niche conservatism may be a common outcome in alpine species diversification.

opencc-zeroFeb 2022View details →

Data from: Effects of input data sources on species distribution model predictions across species with different distributional ranges

Species distribution models (SDMs) are a popular tool in theoretical and quantitative ecology, and constitute the most widely used modelling framework in global change science and biodiversity conservation. As main data sources, SDMs require georeferenced biodiversity observations as a response or dependent variable (e.g. species occurrence, species richness, etc) and geographic layers of environmental information as predictors or independent variables (e.g. climate, land cover, vegetation indices derived from remote sensing, etc). However, although SDMs have become one of the most important quantitative tools for addressing regular and timely biodiversity assessments worldwide, these techniques are still subject to different sources of uncertainty that have been unequally assessed. Thus, despite uncertainty related to niche-based or distribution-based models has been addressed at different stages in the modelling process, an analysis of the effect of uncertainty coming from alternative data sources on the predictive ability of SDMs is still limited. Citizen-collected species occurrence data (e.g. eBird) are often used for fitting SDMs when data from standardized and expert-supported surveys (e.g. Atlases) are unavailable. On the other hand, macroclimate variables are much more commonly used as predictors in SDMs than other sources of information coming from remote sensing data. We assessed the effects of using different data sources (in both response and predictor variables) on SDM performance across a wide range of bird species with contrasting distributional ranges in the Iberian Peninsula (Portugal and Spain). To do that, a SDM ensemble-forecasting approach was implemented by using bird data from two different data sources: the semi-structured eBird project and standardized Atlases. We fitted SDMs with three predictor types: macroclimate, remotely sensed ecosystem functional attributes (EFAs) from vegetation indices, and their combination. Species were grouped in four range size classes. We also used different evaluation metrics to better assess the uncertainty of model predictions. We then applied generalized linear mixed-effects models to test the effect on model performance of input data source across distributional range sizes while accounting for different accuracy metrics. Pairwise comparisons between range projections were used to assess their spatial similarity. Our models demonstrated the usefulness and complementarity of different input data sources when modelling species distribution across different distributional ranges. Citizen science and remote sensing data contribute to update the knowledge of the distribution of the most threatened bird species by increasing the model accuracy. These findings highlight the need to integrate different data sources to improve the model predictions at regional scale. Our framework also underlines that model uncertainty should be examined more exhaustively at early stages of the modelling process. To perfom and replicate this study, this dataset provides all needed files (as tables) to fit SDMs: i) the Iberian bird species occurrences at 10km UTM square as a response or dependent variable; ii) the geographic layers of environmental information at 10km UTM square for the Iberian Peninsula as predictors or independent variables, such as climate data, ecosystem functioning attributes (EFAs) and the combined climate and EFA data. The dataset is provided by four *.csv files named as: 1) The_Iberian_bird_species_occurrences_dataset_10km.csv 2) CHELSA_bioclimate_variables_IP10km.csv 3) MODIS_EVI-based_EFAs_IP10km.csv 4) Combined_bioclimate_EFA_dataset_IP10km.csv For a more detailed description of the main dataset and each of these subdatasets, please refer to the attached README file. Keywords: bird atlas, eBird data, ecosystem functional attributes (EFAs), Iberian Peninsula, IUCN categories, Model accuracy, MODIS EVI, narrow-ranged species, remote sensing, species distribution models (SDMs), widespread species

opencc-zeroMar 2022View details →

Ignoring species availability biases occupancy estimates in single-scale occupancy models

1. Most applications of single-scale occupancy models do not differentiate between availability and detectability, even though species availability is rarely equal to one. Species availability can be estimated using multi-scale occupancy models, and the availability process includes elements of species movement, behavior, and phenology. However, for the practical application of multi-scale occupancy models, it can be unclear what a robust sampling design looks like and what the statistical properties of the multi-scale and single-scale occupancy models are when availability is less than one. 2. Using simulations, we explore the following common questions asked by ecologists during the design phase of a field study: (Q1) what is a robust sampling design for the multi-scale occupancy model when there are a priori expectations of parameter estimates?, (Q2) what is a robust sampling design when we have no expectations of parameter estimates?, and (Q3) can a single-scale occupancy model with a random effects term adequately absorb the extra heterogeneity produced when availability is less than one and provide reliable estimates of occupancy probability?. 3. Our results show that there is a tradeoff between the number of sites and surveys needed to achieve a specified level of acceptable error for occupancy estimates using the multi-scale occupancy model. We also document that when species availability is low (< 0.40 on the probability scale), then single-scale occupancy models underestimate occupancy by as much as 0.40 on the probability scale, produce overly precise estimates, and provide poor parameter coverage. This pattern was observed when a random effects term was and was not included in the single-scale occupancy model, suggesting that adding a random-effects term does not adequately absorb the extra heterogeneity produced by the availability process. In contrast, when species availability was high (> 0.60), single-scale occupancy models performed similarly to the multi-scale occupancy model. 4. As a companion, we provide an RShiny app that allows users to further explore our results and sampling designs across a number of different scenarios <a href="https://gdirenzo.shinyapps.io/multi-scale-occ/">https://gdirenzo.shinyapps.io/multi-scale-occ/</a>. Our results suggest that unaccounted for availability can lead to underestimating species distributions when using single-scale occupancy models, which can have large implications on ecological inference and predictions for practitioners, such as those working at the front lines of invasion ecology, disease emergence, and species conservation.

Fig.1 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance

Fig.1. Sampling sites for Vestia turgida in Ukraine (photo by O. Baidashnikov).

Fig. 5 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance

Fig. 5. Partial dependence plot for terrain roughness index (tri).

Fig. 7 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance

Fig. 7. Partial dependence plot for silt content (SLT).

Fig. 6 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance

Fig. 6. Partial dependence plot for pH water (phh2o).

Fig. 11 in Modelling The Bioclimatic Niche Of A Cohort Of Selected Mite Species (Acari, Acariformes) Associated With The Infestation Of Stored Products

Fig. 11. Response of Gl. domesticus to maxTempColdestMonth.

Fig. 10 in Modelling The Bioclimatic Niche Of A Cohort Of Selected Mite Species (Acari, Acariformes) Associated With The Infestation Of Stored Products

Fig. 10. Response of L. destructor to continentality.

Fig. 6 in Modelling The Bioclimatic Niche Of A Cohort Of Selected Mite Species (Acari, Acariformes) Associated With The Infestation Of Stored Products

Fig. 6. Response of Gl. domesticus to PETcoldQ.

Fig. 8 in Modelling The Bioclimatic Niche Of A Cohort Of Selected Mite Species (Acari, Acariformes) Associated With The Infestation Of Stored Products

Fig. 8. Response of L. destructor to aridityIndexThornthwaite.

Fig. 7 in Modelling The Bioclimatic Niche Of A Cohort Of Selected Mite Species (Acari, Acariformes) Associated With The Infestation Of Stored Products

Fig. 7. Response of A. siro to aridityIndexThornthwaite.

Fig. 5 in Modelling The Bioclimatic Niche Of A Cohort Of Selected Mite Species (Acari, Acariformes) Associated With The Infestation Of Stored Products

Fig. 5. Response of L. destructor to PETcoldQ.

Fig. 4 in Modelling The Bioclimatic Niche Of A Cohort Of Selected Mite Species (Acari, Acariformes) Associated With The Infestation Of Stored Products

Fig. 4. Response of A. siro to PETcoldQ.

Fig. 3 in Modelling The Bioclimatic Niche Of A Cohort Of Selected Mite Species (Acari, Acariformes) Associated With The Infestation Of Stored Products

Fig. 3. Average monthly relative humidity.

Fig. 2 in Modelling The Bioclimatic Niche Of A Cohort Of Selected Mite Species (Acari, Acariformes) Associated With The Infestation Of Stored Products

Fig. 2. Average monthly precipitation.

Fig.1 in Modelling The Bioclimatic Niche Of A Cohort Of Selected Mite Species (Acari, Acariformes) Associated With The Infestation Of Stored Products

Fig.1. Average monthly temperature.