Loading [Contrib]/a11y/accessibility-menu.js
Research article

Modeling the impacts of climate change on Species of Concern (birds) in South Central U.S. based on bioclimatic variables

  • Received: 29 November 2016 Accepted: 30 March 2017 Published: 31 March 2017
  • We used 19 bioclimatic variables, five species distribution modeling (SDM) algorithms, four general circulation models, and two climate scenarios (2050 and 2070) to model nine bird species. Identified as Species of Concern (SOC), we highlighted these birds: Northern/Masked Bobwhite Quail (Colinus virginianus), Scaled Quail (Callipepla squamata), Pinyon Jay (Gymnorhinus cyanocephalus), Juniper Titmouse (Baeolophus ridgwayi), Mexican Spotted Owl (Strix occidentalis lucida), Cassin’s Sparrow (Peucaea cassinii), Lesser Prairie-Chicken (Tympanuchus pallidicinctus), Montezuma Quail (Cyrtonyx montezumae), and White-tailed Ptarmigan (Lagopus leucurus). The Generalized Linear Model, Random Forest, Boosted Regression Tree, Maxent, Multivariate Adaptive Regression Splines, and an ensemble model were used to identify present day core bioclimatic-envelopes for the species. We then projected future distributions of suitable climatic conditions for the species using data derived from four climate models run according to two greenhouse gas Representative Concentration Pathways (RCPs 2.6 and 8.5). Our models predicted changes in suitable bioclimatic-envelopes for all species for the years 2050 and 2070. Among the nine species of birds, the quails were found to be highly susceptible to climate change and appeared to be of most future conservation concern. The White-tailed Ptarmigan would lose about 62% of its suitable climatic habitat by 2050 and 67% by 2070. Among the species distribution models (SDMs), the Boosted Regression Tree model consistently performed fairly well based on Area Under the Curve (AUC range: 0.89 to 0.97) values. The ensemble models showed improved True Skill Statistics (all TSS values > 0.85) and Kappa Statistics (all K values > 0.80) for all species relative to the individual SDMs.

    Citation: Eric Ariel L. Salas, Virginia A. Seamster, Kenneth G. Boykin, Nicole M. Harings, Keith W. Dixon. Modeling the impacts of climate change on Species of Concern (birds) in South Central U.S. based on bioclimatic variables[J]. AIMS Environmental Science, 2017, 4(2): 358-385. doi: 10.3934/environsci.2017.2.358

    Related Papers:

    [1] Oltion Marko, Joana Gjipalaj, Dritan Profka, Neritan Shkodrani . Soil erosion estimation using Erosion Potential Method in the Vjosa River Basin, Albania. AIMS Environmental Science, 2023, 10(1): 191-205. doi: 10.3934/environsci.2023011
    [2] Alma Sobrino-Figueroa, Sergio H. Álvarez Hernandez, Carlos Álvarez Silva C . Evaluation of the freshwater copepod Acanthocyclops americanus (Marsh, 1983) (Cyclopidae) response to Cd, Cr, Cu, Hg, Mn, Ni and Pb. AIMS Environmental Science, 2020, 7(6): 449-463. doi: 10.3934/environsci.2020029
    [3] Raden Darmawan, Sri Rachmania Juliastuti, Nuniek Hendrianie, Orchidea Rachmaniah, Nadila Shafira Kusnadi, Ghassani Salsabila Ramadhani, Yawo Serge Marcel, Simpliste Dusabe, Masato Tominaga . Effect of electrode modification on the production of electrical energy and degradation of Cr (Ⅵ) waste using tubular microbial fuel cell. AIMS Environmental Science, 2022, 9(4): 505-525. doi: 10.3934/environsci.2022030
    [4] Seiran Haghgoo, Jamil Amanollahi, Barzan Bahrami Kamangar, Shahryar Sorooshian . Decision models enhancing environmental flow sustainability: A strategic approach to water resource management. AIMS Environmental Science, 2024, 11(6): 900-917. doi: 10.3934/environsci.2024045
    [5] M.A. Rahim, M.G. Mostafa . Impact of sugar mills effluent on environment around mills area. AIMS Environmental Science, 2021, 8(1): 86-99. doi: 10.3934/environsci.2021006
    [6] Motharasan Manogaran, Mohd Izuan Effendi Halmi, Ahmad Razi Othman, Nur Adeela Yasid, Baskaran Gunasekaran, Mohd Yunus Abd Shukor . Decolorization of Reactive Red 120 by a novel bacterial consortium: Kinetics and heavy metal inhibition study. AIMS Environmental Science, 2023, 10(3): 424-445. doi: 10.3934/environsci.2023024
    [7] Martina Grifoni, Francesca Pedron, Gianniantonio Petruzzelli, Irene Rosellini, Meri Barbafieri, Elisabetta Franchi, Roberto Bagatin . Assessment of repeated harvests on mercury and arsenic phytoextraction in a multi-contaminated industrial soil. AIMS Environmental Science, 2017, 4(2): 187-205. doi: 10.3934/environsci.2017.2.187
    [8] Adrian Schmid-Breton . Transboundary flood risk management in the Rhine river basin. AIMS Environmental Science, 2016, 3(4): 871-888. doi: 10.3934/environsci.2016.4.871
    [9] Jerry R. Miller . Potential ecological impacts of trace metals on aquatic biota within the Upper Little Tennessee River Basin, North Carolina. AIMS Environmental Science, 2016, 3(3): 305-325. doi: 10.3934/environsci.2016.3.305
    [10] Maja Radziemska, Agnieszka Bęś, Zygmunt M. Gusiatin, Jerzy Jeznach, Zbigniew Mazur, Martin Brtnický . Novel combined amendments for sustainable remediation of the Pb-contaminated soil. AIMS Environmental Science, 2020, 7(1): 1-12. doi: 10.3934/environsci.2020001
  • We used 19 bioclimatic variables, five species distribution modeling (SDM) algorithms, four general circulation models, and two climate scenarios (2050 and 2070) to model nine bird species. Identified as Species of Concern (SOC), we highlighted these birds: Northern/Masked Bobwhite Quail (Colinus virginianus), Scaled Quail (Callipepla squamata), Pinyon Jay (Gymnorhinus cyanocephalus), Juniper Titmouse (Baeolophus ridgwayi), Mexican Spotted Owl (Strix occidentalis lucida), Cassin’s Sparrow (Peucaea cassinii), Lesser Prairie-Chicken (Tympanuchus pallidicinctus), Montezuma Quail (Cyrtonyx montezumae), and White-tailed Ptarmigan (Lagopus leucurus). The Generalized Linear Model, Random Forest, Boosted Regression Tree, Maxent, Multivariate Adaptive Regression Splines, and an ensemble model were used to identify present day core bioclimatic-envelopes for the species. We then projected future distributions of suitable climatic conditions for the species using data derived from four climate models run according to two greenhouse gas Representative Concentration Pathways (RCPs 2.6 and 8.5). Our models predicted changes in suitable bioclimatic-envelopes for all species for the years 2050 and 2070. Among the nine species of birds, the quails were found to be highly susceptible to climate change and appeared to be of most future conservation concern. The White-tailed Ptarmigan would lose about 62% of its suitable climatic habitat by 2050 and 67% by 2070. Among the species distribution models (SDMs), the Boosted Regression Tree model consistently performed fairly well based on Area Under the Curve (AUC range: 0.89 to 0.97) values. The ensemble models showed improved True Skill Statistics (all TSS values > 0.85) and Kappa Statistics (all K values > 0.80) for all species relative to the individual SDMs.


    1. Introduction

    Asopos River basin, in East-Central Greece is characterized by a recorded problem of hexavalent chromium contamination, exceeding in some cases the value of 100 μg L−1 as measured in groundwater samples collected from the area [1]. Geogenic and anthropogenic components have contributed to the recorded high levels of chromium contamination in the Asopos River Basin. The geological character of surrounding area of Asopos River basin mainly is Neogene lake-shallow marine sediments, clastic formations of continental origin and parts of ophiolite complexes [2]. The detection of elements, such as Cr and Ni in soils and waters, has often a strong lithogenic origin correlated to the existence of ophiolite outcrops composed by ultramafic rocks [3], but also Fe-Ni deposits [4,5]. Cases where occurrence of hexavalent chromium is primarily of geogenic origin have also been documented for California [6,7,8], New Caledonia [9], Zimbabwe [10], Italy [11], etc. The geogenic mobilization of Cr(Ⅵ) from highly insoluble Cr(Ⅲ) minerals, like chromite, takes place via a two-stage mechanism [10]. At first Cr(Ⅲ) in the matrix of chromite is hydrolyzed to Cr(OH)3. The following stage is the oxidation of Cr(Ⅲ) to Cr(Ⅵ) under the action of easily reducible Mn oxides (the mixed Mn(Ⅱ)/Mn(Ⅲ) oxide hausmannite (Mn3O4) or the Mn(Ⅲ) oxide manganite (MnOOH)). It is considered [10] that this natural process is probably continuous in concretionary subsoils subject to wetting-drying cycles.

    Industrialization in the Asopos River Basin area started in the early 1960’s and today more than 400 installations exist in the area. Metal finishing and manufacturing plants, often using Cr-based chemicals in their processes were major chromium polluters in the area. All facilities were obliged to treat their effluents in-house in appropriate wastewater treatment units, but until 2008 the treated effluent was allowed to be discharged underground via disposal in absorption type sinks. As a result of this the anthropogenic factor, the observed Cr contamination in Asopos River basin cannot be neglected. University of Athens research [12] suggests that the Asopos river sediments are enriched with Cr and Ni by a factor of almost 2.5 compared to the local background values.

    The objective of this work was to:

    investigate whether previous disposal practices in four (4) metal finishing facilities have led to potential contamination to the adjacent soils, and

    to compare the potential contaminated soil concentration values of the metal finishing facilities with

    ❖ greater area background metal concentration values

    ❖ Potentially polluted and newly investigated soil metal concentration values of the Inofyta Industrial Area (IIA).

    The four investigated metal finishing industrial sites are Hellenic Aerospace Industry S.A. (designated as HAI), Europa Profile Aluminium S.A. (EU), Aluminco S.A. (AL) and Viometale S.A. (Ⅵ) [13,14,15,16]. The reason for selecting these metal finishing sites is:

    HAI, EU, AL are three of the larger installations in the area using Hexavalent Chromium

    In Ⅵ a discrete thin metal contamination layer on surface soils was found to the south of the area during a Prefecture Environmental Audit.

    Prefecture of Sterea Ellada(relevant Environmental Authority) considers HAI, EU, AL and VI as priority potential polluters.


    2. Materials and Methods


    2.1. Study Sites, sampling strategy and data collected

    The study area is focusing on the four above mentioned metal finishing industrial sites HAI, EU, AL VI and the Inofyta Industrial Area (IIA). Sampling strategy involved the collection of three groups of soil samples and for comparison reasons the data collected from Inofyta industrial area by the research team of the EU funded project LIFE-CHARM “Chromium in Asopos groundwater system: Remediation technologies and Measures” [17] (data available at http://www.charm-life.gr/charm/index.php/en/documents) were also evaluated, called as fourth group (LGR-4) sampling. The first group (GR-1) assumed free of anthropogenic influence was intended to represent natural geochemical background values close to the industrial sites. Selected sampling points (depicted as HR, ER, AR and VR in Figure 1) were in the vicinity of the metal finishing and industrial sites but not affected from any potential polluting factors. The second group (GR-2), collected in the period 2011-2012 (campaigns by Sybilla Ltd), and consisted of samples from areas suspected of pollution from ongoing activities or historical disposal practices. Samples are either samples from soil shallow layers, i.e. 0-80 cm, or soil core samples from boreholes, up to a depth of 15 meters. The greater Asopos river Basin area Industrial Sites, and location of investigated surface soil sampling points of investigated metal finishing units of GR-1 (HR1-VR1-ER1-ER2-ER3-ER4-ER5-ER6, AR1.) and GR-2 (HB1-HB2-HB3, VB2-VB2, EB1-EB2-EB3-EB4, AB1-AB2-AB3-AB4, HR1-VR1-ER1-ER2-ER3-ER4-ER5-ER6, AR1) group campaigns are presented in Figure 1. GR-1, GR-2, GR-3 and LGR-4 sampling locations and the total number of analyzed samples per industrial site are presented in Tables 1-4.

    The third group (GR-3) consisted of more than 10 samples collected in summer 2015 (campaigns by Sybilla ltd in the framework of EU IED Directive [18]) Baseline Site Investigation Study [19,20]) from areas suspected of pollution from ongoing activities or previous disposal practices. Samples are either soils collected from the shallow layers, i.e. 0-80 cm, or core samples from boreholes, drilled down to a depth of about 15 meters. The location of boreholes is shown in Figure 2.

    For comparison reasons data collected from Inofyta industrial area by the research team of the EU funded project LIFE-CHARM “Chromium in Asopos groundwater system: Remediation technologies and Measures” [17] (data available at http://www.charm-life.gr/charm/index.php/en/documents) were also evaluated. This Life Project, fourth group campaign (LGR-4) of samples was collected at the period 2011-2012. A sampling program was carried out during which seven (7) new groundwater wells with a depth of approximately 30-50 m were drilled at Inofyta industrial area (N1, N2, N3, N4, N5, N6, N7), between January and February 2012. During the construction, drill core samples were collected from each borehole and relevant chemical analyses followed. Boreholes sampling was followed by a surface soil sampling where a series of 12 surface soil samples were also collected during this action in order to investigate the presence of Cr.The location of relevant boreholes is shown in Figure 3.

    Figure 1. First (GR-1) and Second (GR-2) Group Campaigns. Investigated metal finishing installation sites (a-total sampling area, b- Hellenic Aerospace Industry S.A-HAI area, d-Europa S.A.-EU area, e-Aluminco S.A.-AL area and c-Viometale S.A.-VI area) and boreholes (GR-2 sampling points HB1-HB2-HB3, VB2-VB2, EB1-EB2-EB3-EB4, AB1-AB2-AB3-AB4) and surface soil sampling points (GR-1 and GR-2 sampling points HR1-VR1-ER1-ER2-ER3-ER4-ER5-ER6, AR1) are depicted
    Figure 2. Location of investigated surface soil sampling points for the Europa SA. (EU) and Aluminco SA (AL). Sybilla 2015. Third Group (GR-3) campaign
    Figure 3. Location of investigated industrial sites and surface soil sampling points. LIFE-CHARM 2012. Fourth Group (LGR-4) Campaign

    2.2. Parameters analyzed and analytical methods used.

    Laboratories involved in the chemical analysis of the collected soil samples, the analytical methods used, and the parameters analyzed, are presented in Tables 1-4.

    Table 1.Number of samples, parameters analyzed and analytical methods used. GR-1 Campaign. Sampling locations assumed free of anthropogenic influence (Boreholes at HAI, EU, AL, VI and additional Surface Soil samples at EU)
    Site No of sampling locations (no of samples) Parameters analyzed Methods Labs (*)
    HAI 1 (1) Cr, Ni, Cu, Zn, Pb, Al Digestion with AR(a) Andreou
    Cr(Ⅵ) Elution with water(b)
    Europa 6 (6) Cr, Ni Digestion with AR(a) Andreou
    Cr(Ⅵ) Elution with water(b)
    Aluminco 1 (1) Cr, Ni, Fe, Al Digestion with AR(a)XRF(d) EuF/LabMet
    Cr(Ⅵ) Alkaline digestion(c) LabMet
    Viometale 1 (1) Cr, Ni, Cu, Zn, etc.Cr(Ⅵ) XRF(d), AR(a)Alkaline digestion(c) LabMet
     | Show Table
    DownLoad: CSV
    Table 2.Number of samples, parameters analyzed and analytical methods used. GR-2 Campaign
    Site No of sampling locations (no of samples) Parameters analyzed Methods Labs (*)
    Un-contaminated Suspected for contamination
    HAI 1 (7) 3 (42) Cr, Ni, Cu, Zn, Pb, Al Digestion with AR(a) Andreou
    Cr(Ⅵ) Elution with water(b)
    Europa 6 (13) 4 (49) Cr, Ni Digestion with AR(a) Andreou
    Cr(Ⅵ) Elution with water(b)
    Aluminco 1 (6) 4 (12) Cr, Ni, Fe, Al Digestion with AR(a)XRF(d) EuF/LabMet
    Cr(Ⅵ) Alkaline digestion(c) LabMet
    Viometale 1 (4) 6 (19) Cr, Ni, Cu, Zn, etc. XRF(d)AR(a) LabMet
    Cr(Ⅵ) Alkaline digestion(c)
     | Show Table
    DownLoad: CSV
    Table 3.Number of samples, parameters analyzed and analytical methods used. GR-3 Campaign
    Site No of sampling locations (no of samples) Parameters analyzed Methods Labs (*)
    Un-contaminated Suspected for contamination
    Europa 2 3 Cr, Ni, Fe, Al Digestion with AR(a) LabMet
    Cr(Ⅵ) Alkaline digestion(c)
    Aluminco 1 5 Cr, Ni, Fe, Al Digestion with AR(a) LabMet
    Cr(Ⅵ) Alkaline digestion(c)
     | Show Table
    DownLoad: CSV
    Table 4.Number of samples, parameters analyzed and analytical methods used at the EU funded Life Project Group LGR-4 Campaign. (Boreholes and Surface Soil samples)
    Site No of sampling locations (no of samples) Parameters
    analyzed
    Methods Labs(*)
    Un-contaminated Suspected for contamination
    Boreholes - 38 Cr, Ni, Fe, Al XRF(d) LabMet
    Cr(Ⅵ) AR(a)
    Surface Soil - 12 Cr, Ni, Fe, Al XRF(d) LabMet
    Cr(Ⅵ) AR(a)
    (a) Digestion with aqua regia followed by determination of metals in solution by AAS or ICP-MS (EN 13657)
    (b) Elution with water, determination of soluble Cr(Ⅵ) (DIN 38405-24: 05.87, AWWA-3500-Cr/B)
    (c) Alkaline digestion, determination of extracted Cr(Ⅵ) (USEPA, SW-846 Methods 3060A and 7196)
    (d) Determination of total elements concentration by X-ray fluorescence spectrometry (EN 15309)
    (*) Laboratories: (a) Andreou, K. Andreou. Ltd, Athens, (b) EuF: Eurofins Umwelt Ost GmbH, Jena, Germany, (c) LabMet: Laboratory of Metallurgy, NTUA, Athens.
    For the majority of samples, namely those collected from HAI, Europa and Aluminco, the elemental analysis was carried out following the digestion of samples with aqua regia (AR). The samples collected from Viometale were analyzed by X-ray fluorescence (XRF) spectrometry, (mainly due to time constraints - XRF analysis is much more rapid, as there is no need for any pretreatment steps, such as acid leaching or fusion). The LIFE-CHARM samples were also analyzed by XRF.
     | Show Table
    DownLoad: CSV

    3. Results


    3.1. Cr and Ni soil background concentrations

    Analysis by XRF determines the total content of elements in the solid samples, which does not coincide with the amount extracted by aqua regia. As discussed in a previous paper [21], using GR-1 and GR-2 campaigns results and a dataset of 40 surface soil samples collected throughout the whole Greek territory in the framework of the Geochemical Atlas of Europe [22], the concentration of chromium determined by the method of aqua regia digestion, Cr (AR), is about 4 times less compared to the total content of Cr determined by XRF, Cr (XRF). This can be attributed to the fact that the highest percentage of chromium in Greek soils is incorporated into insoluble minerals, e.g. substituted aluminosilicates or spinel minerals like chromite, which are not affected by the AR digestion. On the contrary, the total amount of Ni in soils is soluble in AR. As a consequence, the concentration of Ni determined by the AR digestion method, Ni(AR), is very close to the total content, as determined by XRF, Ni (XRF).

    For the assessment of Cr and Ni background concentration values in Asopos River Basin area soils, we quote data collected from various references consisting of various soil samples analyzed from locations which were assumed free of contamination from industrial activities. Total Cr, Ni and Cr (Ⅵ) concentrations are presented in the following Table 5.

    Table 5.Analyses of Asopos reference soils (n = 30 samples) compared to Cr and Ni values determined in soils of adjacent areas and in all Greece [3,21,24]
    Area
    (Number of samples)
    Cr
    (mg/kg)
    Ni
    (mg/kg)
    Cr(Ⅵ)
    (mg/kg)
    Source
    Range Mean Range Mean Range
    Asopos
    (n = 30)
    60-410 220 91-1200 620 >0.1-9.3 (a) [21]
    Oropos
    (n = 33)
    17-600 212 -- [3]
    Thebes
    (n = 51)
    134-856 277 621-2639 1591 -- [23]
    Atalante
    (n = 64)
    48-4200 453 44-2730 533 -- [24]
    All Greece
    (n = 41)
    2-466 102 2-1812 171 -- [22]
    (a) Cr(Ⅵ) detected in 3 among the 30 analyzed reference soils (5.5, 6.0 and 9.3 mg/kg).
     | Show Table
    DownLoad: CSV

    Chromium concentration values determined by aqua regia (AR) method are similar to the concentration levels determined at Oropos [3] and at Thebes [24], adjacent to the study area, with similar geological formations. As far as Cr (Ⅵ) is concerned, this species was detected only in three (3) among the 26 analyzed samples of Asopos Area, with concentrations 5.5, 6.0 and 9.3 mg/kg respectively. Ni concentrations determined by aqua regia (AR) method in the Asopos river Basin Area, were significantly higher.

    Since Greece has not yet developed national soil quality guidelines for Cr and Ni, relevant guidelines from three European countries, namely Italy, Germany and Belgium (Wallonia), were used and are presented in Table 6 [25]. These values represent the upper allowed concentration levels of Cr and Ni in soils for residential and industrial land use. Cr(Ⅵ) threshold concentrations limits exist only in the regulations of Wallonia.

    As seen in Table 5, the mean concentration of total Cr in Asopos soils (220 mg/kg), slightly exceeds the Italian threshold limit value for residential areas, but satisfies all other limit values. On the contrary, the mean concentration of Ni (620 mg/kg) exceeds the German limit for industrial areas. In Thebes’s soils, most samples of Ni were exceeding the threshold limits. An analysis of Table 6, ends up to the conclusion that the use of these soil quality guidelines when applied in metalliferous areas, like those encountered in many regions of Greece is questionable (since the geochemical background for at least Cr and Ni elements is often higher).

    Table 6.National Soil quality guidelines in three European Countries (IT = Italy, DE = Germany, BE (W) = Belgium (Wallonia))
    Soil limit values (mg/kg)
    Residential areas* Industrial areas*
    IT DE BE(W) IT DE BE(W)
    Cr 150 400 520 800 1000 700
    Ni 120 140 300 500 900 500
    Cr(Ⅵ) -- -- 4.2 -- -- --
    (*) Cr, Ni digestion of samples with aqua regia (AR).
     | Show Table
    DownLoad: CSV

    3.2. Cr and Ni concentration values in soils potentially contaminated


    3.2.1. Cr and Ni concentration values in soils at Hellenic Aerospace Industry

    Figure 1b presents three boreholes that were drilled at HAI grounds, close to potentially polluting HAI sites, i.e. ponds that were used for the treatment of hazardous industrial wastewater, storage of physicochemically treated wastewater and for the storage and drying of industrial sludge. Total Cr concentration profiles are presented in Figure 4a. Total Chromium, Aqua regia (AR) Cr(AR), concentration values varied from 51 to 281 mg/kg with a mean value of 124 mg/kg. Total Ni aqua regia (AR) Ni (AR), concentration values varied from 132 to 618 mg/kg with a mean value of 262 mg/kg (Figure 5a). All these Cr(AR) and Ni(AR) soil concentration values are of the same order of magnitude with soil concentration values assumed free of anthropogenic contamination. Hexavalent Cr content was not detected in any of the soil samples examined.

    Figure 4. Total Cr (AR) (and Cr(XRF) at Viometal) concentration values and profiles in boreholes drilled in the metal finishing industrial sites of the (HAI) Hellenic Aerospace Industry, Europa, Aluminco and Viometale. Second Campaign GR-2 Group. Dashed lines indicate the range of concentration values measured in the reference soils. (k: soil hydraulic permeability)
    Figure 5. Ni concentration values and profiles in boreholes drilled in the sites of the metal finishing installations of (HAI) Helllenic Aerospace Industry, Europa, Aluminco and Viometale. Second Campaign GR-2 Group. Dashed lines indicate the range of concentration values measured in the reference soils. (k: soil hydraulic permeability)

    3.2.2. Cr and Ni concentration values in soils at Europa Profile Aluminium

    For assessing the potential soil investigation in Europa grounds four boreholes were drilled at locations shown in Figure 1d. Borehole #1 (EB1) was drilled beneath the sink, where physicochemically treated wastewater from the electrostatic coating process was disposed for over two decades. For assessing the potentially contaminated soil directly beneath the sink, one inclined borehole was drilled with a 45o angle (EB4). A schematic drawing of the two above mentioned boreholes is given in Figure 6.

    Figure 6. Boreholes at the metal finishing Europa (EB1 and EB4) beneath the sink of the installation (Ia: silty clay and Ⅱa: marl)

    Borehole EB3 was drilled at a location where a small stream enters the metal finishing Europa installation area whereas borehole EB2 was drilled 60 meters downstream of the treated wastewaters sink. Total Cr concentration values of Europa boreholes are within the background values of the area as presented within this article, except for two soil samples of EB1 (depths of 8 and 11 m), where Cr soil concentration values are 619 and 849 mg/kg respectively. Borehole EB4 (inclined borehole) close to EB1, presents a similar total Cr profile but the Cr soil concentration values at depths of 8 and 11 m lie within the background levels range. It must be noticed that at the bottom of the sink (depth of 4 m) a thin greenish solid layer no more than a few centimeters thickness was found. This layer was a Cr-rich sludge, i.e. Cr concentration value of about 80100 mg/kg of trivalent Cr. It is argued that the treated effluents containing some small amount of suspended sludge solids, had been filtered and retained inside the sink. It must be noticed though that the soil beneath the sink is relatively contamination free. Soil samples at 4, 5 and 6 meters under the sink (boreholes EB1 and EB4) have low Cr concentration values, ranging from 100 to 280 mg/kg. Ni soil concentration values varied between 257 and 1080 mg/kg and were close to Ni concentration values in reference soils (Figure 5b). Hexavalent chromium was found in 15 of the 49 samples, at a maximum soil concentration value of 10.1 mg/kg, close to reference soil samples.

    Five surface soil samples were analyzed during the Fourth Group GR-4 campaign at Europa Profile Aluminium grounds, within the facility borders. Chromium concentration values are shown in Figure 7. Chromium, Cr (AR), concentration varied between 95 and 495 mg/kg with a mean value of 247 mg/kg. All these values but one are within the range of concentrations measured in the uncontaminated reference soils. Hexavalent Cr (Ⅳ) soil concentration values were below detection limit in any of these samples examined.

    Figure 7. Total Cr concentration values in Sybilla 2015 Fourth Group GR-4 Campaign surface samples. Europa Profile Aluminium (Locations in Figure 2). Dashed lines indicate German Soil Threshold concentration values for different land uses

    3.2.3. Cr and Ni concentration values in soils at Aluminco

    The metal finishing unit Aluminco disposed its physicochemicaly treated industrial wastewater in two parallel sinks, at a depth of 4 m, from 2003 until May 2008. For the assessment of potentially contaminated soil four boreholes, inclined with slopes 60o-75o, were drilled (two boreholes per sink), as shown in Figure 1e. There exists a thin layer of polluted soil just at the bottom of the sinks that seems to be affected due to the entrainment of suspended solids. Soil samples were collected below the sinks, from depths 4 m to about 10 m. Soil concentration values of total Cr profiles are shown in Figure 4c. At depth of 4 m, boreholes AB4 and AB3 soil samples have high total Cr (AR) concentration values between 710 mg/kg and 2010 mg/kg, while borehole AB2 Cr soil concentration value was slightly above the reference range. It appears that the treated effluents disposal has an impact on a limited depth soil layer below the sink since the samples of depths at 5, 6 and 9 meters had soil Cr concentration values from 200 to 380 mg/kg. Soil concentration values of Hexavalent chromium (analyzed with the alkaline digestion method) was found to be from 0.2 to 4 mg/kg. Soil concentration values of Ni, ranged from 830 to1650 mg/kg, as presented in Figure 5c. The two Ni highest soil concentration values 1500 and 1650 mg/kg exceed the range of soil concentration values measured in Asopos river Basin reference soils, while similar levels of Ni soil concentration values were measured in soils assumed free of contamination near Thebes and thus a geogenic origin cannot be excluded [23].

    Ten surface soil samples were analyzed during Fourth Group (GR-4) campaign at Aluminco grounds, and their locations are presented in Figure 2, within the facility borders. Chromium soil concentration values are shown in Figure 8. Chromium, Cr (AR), concentration varied between 108 and 327 mg/kg with a mean value of 197 mg/kg. All these values are within the range of concentrations measured in the uncontaminated reference soils. Hexavalent Cr (Ⅳ) soil concentration values were below detection limit in any of these samples examined.

    Figure 8. Cr concentration values in Sybilla 2015 Fourth Group Campaign surface samples. Aluminco (Locations in Figure 2). Dashed lines indicate German Soil Threshold concentration values for different land uses

    3.2.4. Cr and Ni concentration values in Viometale soils

    As mentioned before in Viometale site a discrete thin metal contamination layer on surface soils was found to the south of the area during a Prefecture Environmental Audit. Two boreholes were drilled in metal finishing unit Viometale grounds for assessing potentially contaminated soils. These boreholes, denoted as VB1 and VB2, were drilled near and beneath a sink and shown in Figure 1c. Figure 1c, depicts the surface (and low depth (0-0.8m)) soil samples noted as VS1, VS2, VS3 and VS4 samples. Samples VS1 to VS4 are assumed to be representative of incoming pollution located at a land point receiving runoff water from nearby fields, and the outlet of a duct, draining storm water situated to the north of the national road. Boreholes VB1 and VB2 soil Cr concentration values and profiles are depicted in Figure 4d. Since the metal finishing unit at Viometale does not use any trivalent or hexavalent Cr-based chemicals, Cr soil concentrations were measured for comparison reasons, while pollutant of concern (POC) related to these industrial operations is mainly Ni. Measured Cr(XRF) soil concentration values varied from 234 to 2950 mg/kg, equivalent to soil concentration values 58-738 mg/kg of Cr (AR), since Cr(XRF) soil concentration values are approximately 4 times higher to Cr(AR) values. Soil concentrations of Ni remained within the range of reference soils, as shown in Figure 5d and varied from 230 to 1064 mg/kg. Soil surface and low depth samples at points VS1, VS2, VS3 and VS4, end up to the conclusion that there exists high Ni soil contamination mainly in the upper 40 cm soil layer, measured Ni soil concentration values up to 10340 mg/kg.


    3.2.5. Inofyta Industrial Area (IIA)

    As depicted in Figure 9, Inofyta Industrial Area (IIA) investigationcampaign consisted of samples collected at the period 2011-2012 (LGR-4 campaigns by EU funded project LIFE-CHARM) [17], where

    drill core samples were collected from seven boreholes (N1, N2, N3, N4, N5, N6, N7)

    a series of 12 surface soil samples were also collected

    and relevant chemical analyses followed in order to investigate the presence of Cr.

    The relevant soil Cr (XRF)/Cr (AR) concentration values are presented at Figures 9 and 10. The elevated Cr concentrations demonstrate that the industrial site studied is contaminated. Chromium, Cr (AR), concentration values varied between 51 and 281 mg/kg with a mean value of 430 mg/kg which exceeds slightly the maximum background concentration. 26 of these values are within the range of concentrations measured in the uncontaminated reference soils while 12 exceed the range of background concentrations. Hexavalent Cr (Ⅳ) soil concentration values were below detection limit in of 37 (out of 38) samples examined. Potential sources of this contamination are either buried hazardous wastes or wastewater directly discharged into groundwater.

    Figure 9. Cr profiles in LIFE-CHARM Campaign boreholes N1, N3, N4, N5 drilled in the Inofyta industrial area

    3.2.6. Summary

    Soil samples collected close to the existing wastewater physicochemical treatment unit and the sludge storage facilities at the Hellenic AerospaceIndustry S.A. (HAI) do not seem to indicate soil contamination.

    At the second metal finishing unit under investigation, Europa (EU), disposal of physicochemicaly treated effluents in absorption type sinks led to a thin layer of sludge solids in the bottom of the sink. However, the soil beneath the disposal sink was found to be rather contamination free, and the relevant contamination seems to be localized and not dispersed further.

    Measured soil concentration values at Aluminco (AL) end up to the conclusion that there exists a thin layer of polluted soil just at the bottom of the sinks that seems to be affected due to the entrainment of suspended solids. At lower soil layers the soil concentration values of Cr and Ni varied within the range of reference soils concentration values. So the above mentioned contamination (polluted soil just at the bottom of the sinks), does not seem to have an impact on soil contamination beneath it.

    Figure 10. Cr profiles in LIFE-CHARM Campaign boreholes N2, N6, N7 drilled in the Inofyta industrial area

    Finally, at the last unit analyzed for soil pollution, Viometale (Ⅵ), Ni contamination of surface soils was found to the south of the area. However, the soil near and beneath the sink used in the past for physicochemicaly treated effluents disposal, seems to be contamination free. So the above mentioned contamination (thin discrete layer on the surface soil), does not seem to have an impact on soil contamination beneath it.

    Comparison of soil pollution measurement of the four above mentioned metal finishing units (HAI, EU, AL, VI) with a group of data collected in the framework of EU funded project LIFE-CHARM indicate that the Inofyta Industrial Area (IIA) soil seems to be contaminated. Mean Cr soil concentration values of the boreholes samples slightly exceed the maximum background concentration of the greater area while around 33% of the IIA Cr soil concentration values are well above the maximum background concentration by a number of two.


    4. Conclusions

    Comparison of obtained results allows to ascertain that previous disposal practices at the mentioned four (4) metal finishing facilities HAI, EU, AL and VI have not led to significant potential contamination to the adjacent soils and definitely these installation do not pose a general soil contamination threat to the study area. There was no indication of downstream migration from the land-based treated effluents disposal of the above mentioned facilities. Soil concentration values adjacent to these facilities were rather free of contamination.

    The Cr and Ni soil concentration values in the lower soil layers of the above mentioned metal finishing facilities are of the same order of magnitude with greater area background metal concentration values and significantly lower of documented and newly investigated contaminated soil metal concentration values of the Inofyta Industrial Area (IIA).

    At Inofyta Industrial Area (IIA) the detected soil contamination (measured high Cr soil concentration values) requires special attention for future environmental protection planning actions.


    Conflict of Interest

    The authors declare there is no conflict of interest.


    [1] Huntley B, Collingham YC, Willis SG, et al. (2008) Potential impacts of climatic change on European breeding birds. PLOS ONE 3: e1439. doi: 10.1371/journal.pone.0001439
    [2] Beaumont LJ, Pitman AJ, Poulsen M, et al. (2007) Where will species go? Incorporating new advances in climate modelling into projections of species distributions. Glob Change Biol 13: 1368-1385.
    [3] Marini MA, Barbet-Massin M, Lopes LE, et al. (2009) Predicted climate-driven bird distribution changes and forecasted conservation conflicts in a neotropical savanna. Conserv Biol J Soc Conserv Biol 23: 1558-1567. doi: 10.1111/j.1523-1739.2009.01258.x
    [4] Şekercioğlu ÇH, Primack RB, Wormworth J (2012) The effects of climate change on tropical birds Biol Conserv 148: 1-18.
    [5] Gregory RD (2009) An indicator of the impact of climatic change on European bird populations. PLOS ONE 4: e4678. doi: 10.1371/journal.pone.0004678
    [6] Julliard R, Jiguet F, Couvet D (2004) Evidence for the impact of global warming on the long–term population dynamics of common birds. Proc R Soc Lond B Biol Sci 271: 490-492. doi: 10.1098/rsbl.2004.0229
    [7] Berry PM, Dawson TP, Harrison PA, et al. (2002) Modelling potential impacts of climate change on the bioclimatic envelope of species in Britain and Ireland. Glob Ecol Biogeogr 11: 453-462. doi: 10.1111/j.1466-8238.2002.00304.x
    [8] Thuiller W (2004) Patterns and uncertainties of species' range shifts under climate change. Glob Change Biol 10: 2020-2027. doi: 10.1111/j.1365-2486.2004.00859.x
    [9] Sax DF (2007) Ecological and evolutionary insights from species invasions. Trends Ecol Evol 22: 465-471. doi: 10.1016/j.tree.2007.06.009
    [10] Geyer J (2011) Classification of climate-change-induced stresses on biological diversity. Conserv Biol 25: 708-715. doi: 10.1111/j.1523-1739.2011.01676.x
    [11] Foden WB (2013) Identifying the world's most climate change vulnerable species: A systematic trait-based assessment of all birds, amphibians and corals. PLOS ONE 8: e65427. doi: 10.1371/journal.pone.0065427
    [12] Jeschke JM, Strayer DL (2008) Usefulness of bioclimatic models for studying climate change and invasive species. Ann N Y Acad Sci 1134: 1-24. doi: 10.1196/annals.1439.002
    [13] Dawson TP, Jackson ST, House JI, et al. (2011) Beyond predictions: Biodiversity conservation in a changing climate. Science 332: 53-58. doi: 10.1126/science.1200303
    [14] Rees M, Paull DJ, Carthew SW (2007) Factors influencing the distribution of the yellow-bellied glider (Petaurus australis australis) in Victoria, Australia. Wildl Res 34: 228. doi: 10.1071/WR06027
    [15] Soberón J (2007) Grinnellian and Eltonian niches and geographic distributions of species. Ecol Lett 10: 1115-1123. doi: 10.1111/j.1461-0248.2007.01107.x
    [16] Peterson AT (2011) Ecological Niches and Geographic Distributions. Princeton, N.J: Princeton University Press.
    [17] Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8: 993-1009. doi: 10.1111/j.1461-0248.2005.00792.x
    [18] Beale CM, Lennon JJ, Gimona A (2008) Opening the climate envelope reveals no macroscale associations with climate in European birds. Proc Natl Acad Sci 105: 14908-14912. doi: 10.1073/pnas.0803506105
    [19] Jiménez-Valverde A (2011) Dominant climate influences on North American bird distributions. Glob Ecol Biogeogr 20: 114-118. doi: 10.1111/j.1466-8238.2010.00574.x
    [20] Pearson RG, Dawson TP (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob Ecol Biogeogr 12: 361-371. doi: 10.1046/j.1466-822X.2003.00042.x
    [21] Elith J, Phillips SJ, Hastie T, et al. (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17: 43-57. doi: 10.1111/j.1472-4642.2010.00725.x
    [22] Huntley RE, Green RE, Collinghan Y, et al. (2007) A climatic atlas of European breeding birds. Durham, Sandy and Barcelona: Durham University, 2007.
    [23] Barbet-Massin M, Thuiller W, Jiguet F (2012) The fate of European breeding birds under climate, land-use and dispersal scenarios. Glob Change Biol 18: 881-890. doi: 10.1111/j.1365-2486.2011.02552.x
    [24] Bucklin DN, Basille M, Benscoter AM, et al. (2015) Comparing species distribution models constructed with different subsets of environmental predictors. Divers Distrib 21: 23-35. doi: 10.1111/ddi.12247
    [25] Elith J, Leathwick JR (2009) Species Distribution Models: Ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40: 677-697. doi: 10.1146/annurev.ecolsys.110308.120159
    [26] BISON, 2016. Available from: http://bison.usgs.ornl.gov/#home
    [27] ORNIS, 2016. Available from: http://www.ornisnet.org/
    [28] VertNet, 2016. Available from: http://portal.vertnet.org/search
    [29] GAP Analysis, 2016. Available from: http://gapanalysis.usgs.gov/species/viewer/species-viewer/
    [30] ArcGIS for Desktop, 2016. Available from: http://www.esri.com/software/arcgis/arcgis-for-desktop
    [31] Hijmans RJ, Cameron SE, Parra JL, et al. (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25: 1965-1978. doi: 10.1002/joc.1276
    [32] Dormann CF (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36: 27-46. doi: 10.1111/j.1600-0587.2012.07348.x
    [33] Gama M, Crespo D, Dolbeth M, et al. (2015) Predicting global habitat suitability for Corbicula fluminea using species distribution models: The importance of different environmental datasets. Ecol Model 319: 163-169.
    [34] Talbert C (2012) Software for Assisted Habitat Modeling Package for VisTrails (SAHM: VisTrails). Fort Collins, Colorado, USA: U.S. Geological Survey.
    [35] Poiani KA, Johnson WC (1991) Global warming and prairie wetlands: potential consequences for waterfowl habitat. BioScience 41: 611-618. doi: 10.2307/1311698
    [36] Araújo MB, New M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22: 42-47. doi: 10.1016/j.tree.2006.09.010
    [37] Breiman L (2001) Random forests. Mach Learn 45: 5-32. doi: 10.1023/A:1010933404324
    [38] Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2: 18-22.
    [39] Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77: 802-813. doi: 10.1111/j.1365-2656.2008.01390.x
    [40] Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190: 231-259.
    [41] Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31: 161-175. doi: 10.1111/j.0906-7590.2008.5203.x
    [42] Leathwick JR, Elith J, Hastie T (2006) Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecol Model 199: 188-196. doi: 10.1016/j.ecolmodel.2006.05.022
    [43] Elith J (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129-151. doi: 10.1111/j.2006.0906-7590.04596.x
    [44] Morisette JT (2013) VisTrails SAHM: visualization and workflow management for species habitat modeling. Ecography 36: 129-135. doi: 10.1111/j.1600-0587.2012.07815.x
    [45] Phillips SJ, Dudík M, Elith J, et al. (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl 19: 181-197. doi: 10.1890/07-2153.1
    [46] Species Viewer. SGS National Gap Analysis Program, 2016. Available from: http://gapanalysis.usgs.gov/species/viewer/species-viewer/
    [47] Willey D, Riper CV (2000) First-year movements by juvenile Mexican Spotted Owls in the Canyonlands of Utah. J Raptor Res: 1-7.
    [48] Campbell H, Harris BK (1965) Mass population dispersal and long-distance movements in Scaled Quail. J Wildl Manag 29: 801-805. doi: 10.2307/3798556
    [49] VanDerWal J, Shoo LP, Graham C, et al. (2009) Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know? Ecol Model 220: 589-594. doi: 10.1016/j.ecolmodel.2008.11.010
    [50] Liu C, Berry PM, Dawson TP, et al. (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28: 385-393. doi: 10.1111/j.0906-7590.2005.03957.x
    [51] Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17: 145-151.
    [52] Stohlgren TJ (2010) Ensemble habitat mapping of invasive plant species. Risk Anal 30: 224-235. doi: 10.1111/j.1539-6924.2009.01343.x
    [53] Manel S, Williams HC, Ormerod SJ (2001) Evaluating presence–absence models in ecology: the need to account for prevalence. J Appl Ecol 38: 921-931.
    [54] Rehfeldt GE, Crookston NL, Sáenz-Romero C, et al. (2012) North American vegetation model for land-use planning in a changing climate: a solution to large classification problems. Ecol Appl 22: 119-141. doi: 10.1890/11-0495.1
    [55] Stebbins RC (2003) A Field Guide to Western Reptiles and Amphibians, 3 edition. Boston: Houghton Mifflin Harcourt.
    [56] Warren DL, Seifert SN (2011) Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol Appl 21: 335-342. doi: 10.1890/10-1171.1
    [57] Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43: 1223-1232. doi: 10.1111/j.1365-2664.2006.01214.x
    [58] Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20: 37-46.
    [59] Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240: 1285-1293. doi: 10.1126/science.3287615
    [60] Coetzee BWT, Robertson MP, Erasmus BFN, et al. (2009) Ensemble models predict important bird areas in southern Africa will become less effective for conserving endemic birds under climate change. Glob Ecol Biogeogr 18: 701-710. doi: 10.1111/j.1466-8238.2009.00485.x
    [61] Taylor KE, Stouffer RJ, Meehl GA (2011) An Overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93: 485-498.
    [62] Sheffield J (2013) North American Climate in CMIP5 Experiments. Part I: Evaluation of historical simulations of continental and regional climatology. J Clim 26: 9209-9245.
    [63] Gent PR (2011) The Community Climate System Model Version 4. J Clim 24: 4973-4991. doi: 10.1175/2011JCLI4083.1
    [64] Collins WJ (2011) Development and evaluation of an Earth-System model-HadGEM2. Geosci Model Dev 4: 1051-1075. doi: 10.5194/gmd-4-1051-2011
    [65] Watanabe M. Improved Climate Simulation by MIROC5: Mean States, Variability, and Climate Sensitivity. J Clim 23: 6312-6335.
    [66] Block K, Mauritsen T (2013) Forcing and feedback in the MPI-ESM-LR coupled model under abruptly quadrupled CO2. J Adv Model Earth Syst 5: 676-691. doi: 10.1002/jame.20041
    [67] Roeckner E, Giorgetta MA, Crueger T, et al. (2010) Historical and future anthropogenic emission pathways derived from coupled climate–carbon cycle simulations. Clim Change 105: 91-108.
    [68] Arora VK (2011) Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys Res Lett 38: L05805.
    [69] Chalmers N, Highwood EJ, Hawkins E, et al. (2012) Aerosol contribution to the rapid warming of near-term climate under RCP 2.6. Geophys Res Lett 39: L18709.
    [70] Vuuren DP (2011) The representative concentration pathways: an overview. Clim Change 109: 5-31. doi: 10.1007/s10584-011-0148-z
    [71] Knutti R, Sedláček J (2013) Robustness and uncertainties in the new CMIP5 climate model projections. Nat Clim Change 3: 369-373.
    [72] Hirzel AH, Le Lay G, Helfer V, et al. (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecol Model 199: 142-152. doi: 10.1016/j.ecolmodel.2006.05.017
    [73] Kumar S, Spaulding SA, Stohlgren TJ, et al. (2009) Potential habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US. Front Ecol Environ 7: 415-420. doi: 10.1890/080054
    [74] Capinha C, Anastácio P (2011) Assessing the environmental requirements of invaders using ensembles of distribution models. Divers Distrib 17: 13-24. doi: 10.1111/j.1472-4642.2010.00727.x
    [75] Robert K, Jones DOB, Roberts JM, et al. (2016) Improving predictive mapping of deep-water habitats: Considering multiple model outputs and ensemble techniques. Deep Sea Res Part Oceanogr Res Pap 113: 80-89. doi: 10.1016/j.dsr.2016.04.008
    [76] Marmion M, Parviainen M, Luoto M, et al. (2009) Evaluation of consensus methods in predictive species distribution modelling. Divers Distrib 15: 59-69. doi: 10.1111/j.1472-4642.2008.00491.x
    [77] Poulos HM, Chernoff B, Fuller PL, et al. (2012) Ensemble forecasting of potential habitat for three invasive fishes. Aquat Invasions 7: 59-72. doi: 10.3391/ai.2012.7.1.007
    [78] Du P, Xia J, Zhang W, et al. (2012) Multiple classifier system for remote sensing image classification: A review. Sensors 12: 4764-4792. doi: 10.3390/s120404764
    [79] Meinshausen M (2011) The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim Change 109: 213. doi: 10.1007/s10584-011-0156-z
    [80] Barsugli JJ (2013) The practitioner's dilemma: How to assess the credibility of downscaled climate projections. Eos Trans Am Geophys Union 94: 424-425.
    [81] Dixon KW (2016) Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results? Clim Change 135: 395-408. doi: 10.1007/s10584-016-1598-0
    [82] Stoklosa J, Daly C, Foster SD, et al. (2015) A climate of uncertainty: accounting for error in climate variables for species distribution models. Methods Ecol Evol 6: 412-423. doi: 10.1111/2041-210X.12217
    [83] Bakkenes M, Alkemade JRM, Ihle F, et al. (2002) Assessing effects of forecasted climate change on the diversity and distribution of European higher plants for 2050. Glob Change Biol 8: 390-407. doi: 10.1046/j.1354-1013.2001.00467.x
    [84] Bateman BL, Murphy HT, Reside AE, et al. (2013) Appropriateness of full-, partial- and no-dispersal scenarios in climate change impact modelling. Divers Distrib 19: 1224-1234. doi: 10.1111/ddi.12107
    [85] U.S. Fish and Wildlife Service, Mexican spotted owl (Strix occidentalis lucida) 5-Year review short form summary. Arizona Ecological Services Office, Arizona, USA, 2013.
    [86] Davis DM (2009) Nesting ecology and reproductive success of Lesser Prairie-Chickens in shinnery oak-dominated rangelands. Wilson J Ornithol 121: 322-327. doi: 10.1676/08-090.1
    [87] Jarnevich CS (2016) Assessing range-wide habitat suitability for the Lesser Prairie-Chicken. Avian Conserv Ecol 11: 2.
    [88] Jennison R, Pitman J, Kramer J, et al., Prairie Chicken LEK Survey, Kansas Department of Wildlife, Parks, & Tourism, Kansas, USA, 2013.
    [89] Greenwald N, Petition to list the white-tailed ptarmigan (Lagopus leucura) as a threatened species under the endangered species act. Center for Biological Diversity, Petition, 2010.
    [90] NM Crucial Habitat Assessment Tool, 2016. Available from: http://nmchat.org/.
    [91] Species Reports, 2016. Available from: http://ecos.fws.gov/tess_public/.
    [92] Endangered Species Program, 2016. Available from: http://www.fws.gov/endangered/species/.
    [93] Christmas Bird Count. Audubon, 2016. Available from: https://www.audubon.org/conservation/science/christmas-bird-count.
    [94] Eaton JG, Scheller RM (1996) Effects of climate warming on fish thermal habitat in streams of the United States. Limnol Oceanogr 41: 1109-1115. doi: 10.4319/lo.1996.41.5.1109
    [95] Johnson K, Tonne P, Muldavin E (2004) New Mexico drought at risk species as determined by Natural Heritage New Mexico (NHNM) staff at the University of New Mexico. Museum of Southwestern Biology, Albuquerque, NM, USA, 2004.
    [96] Enquist C, Gori D (2008) Implications of recent climate change on conservation priorities in New Mexico. The Nature Conservancy, Santa Fe, NM, USA, 2008.
    [97] Mitchell NJ, Janzen FJ (2010) Temperature-dependent sex determination and contemporary climate change. Sex Dev Genet Mol Biol Evol Endocrinol Embryol Pathol Sex Determ Differ 4: 129-140.
    [98] La Sorte FA, Jetz W (2010) Projected range contractions of montane biodiversity under global warming. Proc R Soc Lond B Biol Sci: rspb20100612.
    [99] Zack S, Ellison K, Cross M, et al., Climate change planning for the Great Plains: Wildlife vulnerability assessment & recommendations for land and grazing management. Wildlife Conservation Society, North America Program, Bozeman, MT, USA, 2010.
    [100] Coe SJ, Finch DM, Friggens MM (2012) An assessment of climate change and the vulnerability of wildlife in the Sky Islands of the Southwest. Gen. Tech. Rep. RMRS-GTR-273. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 208 p.
    [101] Bagne KE, Finch DM (2013) Vulnerability of species to climate change in the Southwest: threatened, endangered, and at-risk species at Fort Huachuca, Arizona.
    [102] Friggens MM, Finch DM, Bagne KE, et al. (2013) Vulnerability of species to climate change in the Southwest: terrestrial species of the Middle Rio Grande. Gen. Tech. Rep. RMRS-GTR-306. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 191 p.
    [103] Moyle PB, Kiernan JD, Crain PK, et al. (2013) Climate Change Vulnerability of Native and Alien Freshwater Fishes of California: A Systematic Assessment Approach. PLOS ONE 8: e63883. doi: 10.1371/journal.pone.0063883
    [104] Species Data and Modeling, 2016. Available from: http://gapanalysis.usgs.gov/species/data/.
    [105] IUCN Red List of Threatened Species, 2016. Available from: http://www.iucnredlist.org/.
    [106] Nature Serve Explorer, 2016. Available from: http://explorer.natureserve.org/.
  • This article has been cited by:

    1. Ioannis Karaouzas, Natalia Kapetanaki, Angeliki Mentzafou, Theodore D. Kanellopoulos, Nikolaos Skoulikidis, Heavy metal contamination status in Greek surface waters: A review with application and evaluation of pollution indices, 2021, 263, 00456535, 128192, 10.1016/j.chemosphere.2020.128192
  • Reader Comments
  • © 2017 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(9924) PDF downloads(1775) Cited by(20)

Article outline

Figures and Tables

Figures(10)  /  Tables(4)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog