Next Article in Journal
How Rural Communities Relate to Nature in Sub-Saharan Regions: Perception of Ecosystem Services Provided by Wetlands in South-Kivu
Previous Article in Journal
Strengthening AKIS for Sustainable Agricultural Features: Insights and Innovations from the European Union: A Literature Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 16
10.3390/su16167071
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Where Are We Going Now? The Current and Future Distributions of the Monk Parakeet (Myiopsitta monachus) and Eurasian Collared Dove (Streptopelia decaocto) in a Megalopolis

by
Jorge E. Ramírez-Albores
1,*,
Luis A. Sánchez-González
2,
David A. Prieto-Torres
3 and
Adolfo G. Navarro-Sigüenza
2,4
1
Departamento de Botánica, Universidad Autónoma Agraria Antonio Narro, Saltillo 25315, Mexico
2
Museo de Zoología “Alfonso L. Herrera”, Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
3
Laboratorio de Biodiversidad y Cambio Global (LABIOCG), Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Los Reyes Iztacala, Tlalnepantla 54090, Mexico
4
Unidad Multidisciplinaria de Docencia e Investigación, Facultad de Ciencias, Campus Juriquilla, Universidad Nacional Autónoma de México, Juriquilla 76230, Mexico
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(16), 7071; https://doi.org/10.3390/su16167071
Submission received: 24 June 2024 / Revised: 7 August 2024 / Accepted: 9 August 2024 / Published: 17 August 2024
(This article belongs to the Section Sustainability, Biodiversity and Conservation)
Browse Figures
Review Reports Versions Notes

Abstract

:
The monk parakeet (Myiopsitta monachus) and the Eurasian collared dove (Streptopelia decaocto) are two of the most prevalent invasive species globally due to their high dispersal ability. Since these birds were first recorded (1999 for the monk parakeet and 2013 for Eurasian collared dove) in the Mexico City Metropolitan Area (MCMA), both species have spread rapidly throughout the area. However, the impacts of global climate changes on the distribution patterns of these species remain poorly studied across the MCMA. Therefore, based on an ecological niche modeling approach, we assessed the expansion and potential invasion of both species in this megalopolis using current and future climate projections (year 2050). Our results estimated that the current suitable areas are 5564 km2 for the monk parakeet and 5489 km2 for the Eurasian collared dove, covering ~70% of the study area, suggesting a rapidly invading species, as expected. We observed a slight decrease (up to 24%) in both species in future climate scenarios, but our models estimated that the sizes of the suitable areas would remain stable. We found that the range expansion of these species in the megalopolis may be largely attributed to their propensity for jump dispersion and short-time niche expansion ability. Our findings allow for a better understanding of the factors contributing to the range expansion of the monk parakeet and the Eurasian collared dove in Mexico and can better inform the monitoring guidelines for and assessments of these invasive species.

Graphical Abstract

1. Introduction

Alien species are ones that have been introduced by humans into areas outside their original distribution range. Given that the introduced species represent a great threat to global biodiversity [1], only surpassed by climate change and the fragmentation of natural habitats [2,3], the rapid spread of alien species is considered an important component of the global environmental crisis. In addition, their impacts on biodiversity are exceedingly difficult to reverse due to the extent and magnitude they can reach [4]. After their introduction and successful establishment in new environments, alien species become naturalized, managing to expand their populations and participate in biological invasion [5].
Due to their rapid spread and impact after their introduction and successful establishment, the monk parakeet (Myiopsitta monachus) and the Eurasian collared dove (Streptopelia decaocto) are two recent and remarkable cases of biological invasion. These two species have been frequently released into urban and rural environments due to the pet trade, one of the main introduction pathways of invasive alien species [6,7]. Both species appear to have ecological and morphological traits that make them successful invaders across many regions of the world [8,9,10,11,12,13], including rapid adaptability to urban and suburban environments; high growth and reproduction rates; dietary flexibility; and high plasticity and phenotypic flexibility. The biological invasion of M. monachus and S. decaocto into new environments can bring negative ecological impacts, such as competition with native birds for food resources, breeding sites, and nesting materials, leading to the displacement and even local extinction of resident species [8,14,15]. Moreover, invasive species are also a potential vector for diseases that may increase the mortality of native birds and affect human health [15,16,17,18].
The monk parakeet is native to the southern Neotropics in Paraguay, Argentina, Uruguay, Brazil, and Bolivia [19,20], while the Eurasian collared dove is native to Pakistan, India, Sri Lanka, Myanmar, and Bangladesh in southern Asia [21,22]. Myiopsitta monachus and S. decaocto are also ecologically well-distributed, as both are established in different environments throughout the country [23,24,25]. The occurrences of both birds as alien species have been documented in several temperate countries, such as the United States, Spain, and France [9,10,26,27,28,29], where established populations and local expansion dynamics have been reported [30,31,32,33,34]. Both species have experienced the largest population increases recorded among exotic birds in the world, which have allowed them to establish new populations in large cities [10,18,26,35]. For example, M. monachus has become established in several high-latitude, extra-tropical cities, such as New York, Chicago, Portland, and Seattle, USA, and in British Columbia, Canada [24], which has implications for high-altitude areas such as the Mexico City Metropolitan Area.
Since the first record of these invasive birds in Mexico (M. monachus in 1999 and S. decaocto in 2000), worryingly, both species have increased their geographic ranges to more than 95 cities throughout the country [23,24,25]. Regarding their recent introduction into the country, especially in one of the most important megalopolises, Mexico City, M. monachus was first recorded in the northern part in 1999, while S. decaocto was first recorded in the northeastern part in 2013 (Figure 1). One consequence of the expansion of both species is the homogenization of biotic communities [36,37] because invasive species can decrease local biodiversity [38,39,40,41]; such a reduction in local diversity can in turn reduce the resilience of communities to environmental disturbances due to the loss of significant functional traits [42]. In addition, the impacts of global climate changes on the distribution patterns of M. monachus and S. decaocto currently remain poorly studied in urban contexts.
The development of environmental niche modeling tools has facilitated the study of ecological determinants and historical limits [43,44]. Such an approach integrates the study of climatic tolerances through combining species occurrence data and environmental variables over the species’ past, present, and future geographic range [44,45]. This approach utilizes species-specific attributes and spatially explicit climatic attributes to predict the potential spread of the target species in time and space [46]. The results derived from predicted distribution changes can then be used as inputs to objective management plans to control and prevent the expansion of naturalized species [47,48].
Several factors, such as abiotic differences, biotic interactions, dispersal barriers, and human activities, can promote range expansion during invasion processes [49]. This, in turn, can facilitate the colonization of an area with novel combinations of environmental conditions, leading a species to explore the limits of its native niche and to occupy different niches in the newly invaded range [50]. Both invasive alien species in this study have been reported to mostly occupy large urban landscapes from which they later expand into surrounding agricultural areas and other sites highly modified via human activity [33,35,51]. In this study, based on a species distribution modeling approach for these two species, and on factors such as climatic conditions driving species distribution at geographically larger scales [52,53], we assessed the factors underlying the distribution expansion of M. monachus and S. decaocto in high-elevation regions in central Mexico (specifically in the Mexico City Metropolitan Area and adjacent areas), and we identified areas with the highest potential invasion risk for these two species under future climatic scenarios (i.e., the year 2050). Our results may be used to inform efforts to eradicate or control both species, and to mitigate the possible impacts on the native biota and natural ecosystems in this region.

2. Materials and Methods

2.1. Study Area

The Mexico City Metropolitan Area (MCMA) is localized in the Trans-Mexican Volcanic Belt in central Mexico (19°25 N, 99°07 W) (Figure 1). The lowest elevation is 2240 m but increases from the center towards the high mountains (>3900 in the Ajusco volcano). Covering more than 7954 km2, the MCMA is considered one of the largest megalopolises in the world [54] and encompasses large urban areas north of Mexico City and neighboring Mexico State. The rapid growth of the population in the MCMA (more than 22 million, >2559.8 hab/km2) [55,56] has led to the loss and fast transformation of the original habitats, creating a highly homogeneous system with different land use types in which only 34% of the natural vegetation remains [57,58]. Grasslands and desert scrub are the dominant vegetation in the north and east; the south-eastern lowlands are dominated by agricultural fields, and oak pine fir forests are present in relatively large areas in the south, east, and west.

2.2. Occurrence Records for Species

We compiled occurrence records for both M. monachus and S. decaocto from invaded regions across Mexico. These records were obtained from different scientific databases, such as the Atlas of the Birds of Mexico [59], the Christmas Bird Counts (available at https://netapp.audubon.org/cbcobservation/, accessed on 22 January 2024), ORNISnet (available at http://ornisnet.org/, accessed on 22 January 2024), GBIF (Global Biodiversity Information Facility) [60,61], and GAVIA (Global Avian Invasive Atlas) [62], and complemented with citizen databases, such as eBird (available at https://ebird.org/, accessed on 22 January 2024) [24,25] and iNaturalistMX (available at https://mexico.inaturalist.org/, accessed on 22 January 2024). We supplemented these data with information from field surveys performed by JER-A (from January 2015 to December 2023) throughout the MCMA and adjacent areas in Mexico State. We initially obtained 3624 occurrence records for M. monachus and 5766 for S. decaocto.
To clean the databases for model building, duplicate records and those lacking exact geocoordinate data were eliminated. We further reduced the effects of sampling bias using spThin [63] in RStudio [64]. This allowed us to estimate the most appropriate distance among the occurrence data for each species and the nearest neighbor, retaining only those records separated by at least 3 km. Likewise, to identify imprecise species occurrences with incorrect climate values [65], we applied an environmental heterogeneity filter to the occurrence data as implemented in SDMtoolbox in ArcMap 10.2 [66]. After these procedures, we obtained 144 unique occurrence records for M. monachus (from 1999 to 2023; Figure 1A) and 278 for S. decaocto (from 2013 to 2023; Figure 1B) across the MCMA and adjacent areas.

2.3. Current and Future Climatic Scenarios

To characterize Grinnellian niches (see [67,68]) for each species, we used interpolated climate data (~1 km2 cell size resolution) from CHELSA 1.2 (available at https://chelsa-climate.org/, accessed on 22 January 2024). This bioclimatic dataset includes 19 climatic variables summarizing aspects of precipitation and temperature based on the interpolation of data recorded from worldwide meteorological stations spanning a period approximately covering 1980–2010 [69]. Because reducing the multidimensionality and collinearity problems of the variables is a critical part of the modeling process, we selected seven environmental variables to build the potential distribution models for both species (Table 1); to accomplish this, we used a Pearson’s coefficient (r < 0.8) and Variance Inflation Factor [70,71,72]. The selected environmental predictors have been previously regarded as important limiting factors controlling the ecophysiology and distribution of M. monachus and S. decaocto [73,74].
The variables for future climate projections (year 2050) were downloaded from the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report [75]. These scenarios are an important integrating element of the IPCC for understanding the possible climate outcomes, impacts and risks, and mitigation futures [76]. For future climate model simulations, we selected two different climate projections from General Circulations Models (GCMs) GFDL-ESM4 and IPSL-CM6A-LR; both GCMs were used based on three Shared Socio-economic Pathway (SSP) scenarios (SSPs 126, 370 and 585), which assume a high level of greenhouse gas emissions, a medium to high end of plausible future pathways, and a low incidence of climate change mitigation policies, respectively [75,77]. These two models have been identified as more appropriate and useful for ENMs [78,79,80].

2.4. Ecological Niche and Species Distribution Models

We modeled potential climatic suitability areas for both species using MaxEnt v.3.4.1., given its high performance and suitability for presence-only data [81]. The MaxEnt algorithm calculates the maximum entropy probability distribution of suitable conditions for a given species based on a set of climatic data associated with its occurrence data [81,82,83]. To achieve this, we also performed a calibration assessing model complexity [84].
The models were generated using the kuenm R package [72], which enable detailed model calibration and selection (i.e., selecting the best modeling parameters in each case), and the creation of a final model for each species based on each model´s statistical significance, performance, and simplicity. In this manner, we assessed 62 candidate solutions (n = 31 for each species), including a combination of four different sets of environmental predictors, 17 regularization multiplier values [RM: 0.5–8.0], and 11 possible combinations of linear, quadratic, product, and threshold feature types. After this, we selected the best set of candidate solutions showing both a low omission error (<0.05) and low Akaike information criterion (AICc) values [84,85,86,87]. The statistical performance of each model was evaluated using the omission error values [88], the area under the curve (AUC) [89,90], the partial ROC test [91], and TSS (True Skill Statistics) [92].
The final models were transferred to future environmental conditions [87,93,94]. All models were run allowing “unconstrained extrapolation” and “extrapolation by clamping” in MaxEnt projections, which refer to different approaches in handling predictions outside the range of environmental conditions observed in the training data [81,84]. “Unconstrained extrapolation” allows model predictions to extend beyond the range of environmental conditions present in the training data (i.e., areas where environmental conditions might be significantly different from those observed in the training data), while “extrapolation by clamping” restricts predictions to conditions similar to those observed in the training data, minimizing the risk of unrealistic predictions in completely novel environments for each species in future scenarios [81,84]. To provide further reliability of our models when depicting future projections, we performed a multivariate environmental similarity surface (MESS) analysis [95] to help determine areas with higher degrees of uncertainty (high extrapolation values) in the models, offering a view of the novelty of future climate conditions relative to present-day conditions within the calibration area [96].
Then, we visualized the habitat suitability distribution for both species in ArcGIS 10.2 [66]. Based on the continuous suitability score (0–1), we classified the habitat suitability as follows: unsuitable habitat (<0.30), low suitability habitat (0.30 to 0.60), medium-suitability habitat (0.60 to 0.80), and high-suitability habitat (>0.80) [97]. Finally, we generated potential habitat suitability maps, in which the continuous probability maps from 0 to 1 (the logistic response outputs from MaxEnt) [82] were converted into a presence-absence map by setting a decision threshold equal to a 10% omission of the training occurrence data (a 5% false negative rate). This threshold allowed a reduction in commission errors (the false-positive rate) in our final binary maps, resulting in more conservative estimates of species distributions [98].

2.5. Risk of Expansion

To quantify changes in suitable environmental conditions for M. monachus and S. decaocto under climate change, we used SDMtoolbox [65] in ArcGIS 10.2 [66] to compare habitats predicted from current climate conditions with those corresponding to future climate conditions (SSPs 126, 370 and 585). Depending on the differences between current and future scenarios, we divided the results into three categories: gain (an increase in the species’ distribution area), stable (the distribution area was not reduced or shifted), and loss (the distribution area was reduced). In addition, to facilitate the identification of geographic spaces and potential invasion areas, we overlaid the potential distributions for each species with the agricultural areas that were obtained from INEGI (available at https://www.inegi.org.mx/, accessed on 6 February 2024). We also assessed the risk of invasion in protected areas (PAs) using the inventory of the National Commission of Protected Natural Areas (CONANP, available at https://www.gob.mx/conanp, accessed on 6 February 2024).
Finally, to evaluate the expansion of the two invasive species at the regional level, we regressed the annual distances from the first detection of M. monachus and S. decaocto [23,24,25] in the MCMA as a function of time [99]. Annual distances were calculated from the first reported occurrence; from that date onwards, all other occurrences were taken into account individually under the following criteria: only occurrences at a single site were considered, taking into account the oldest occurrence, and data from subsequent occurrences at the same site were discarded; several detections in the same year were considered, as long as they were from sites other than the first occurrence or the oldest detection at the same site. Furthermore, we ran a piecewise linear regression analysis [100,101] in RStudio [64] both to identify significant breaking points and to distinguish different invasion phases. The significance of the breaking points was tested using a Davies test [102]. The slope of each segment provided an estimated rate of spread (V, measured in km/year).

3. Results

3.1. Ecological Models and Geographical Distribution Patterns

Since the first record of occurrence of each species in the MCMA, more than 3600 points of occurrence of M. monachus and more than 5700 of S. decaocto were recorded throughout the metropolitan area through 2023 (Figure 1A,B), with the greatest number of records between 2019 and 2023, representing more than 60% of the total records. Furthermore, 185 and 153 of these records for M. monachus and S. decaocto, respectively, were found in agricultural areas and protected areas.
We detected good model performance under the selected parameter settings for both M. monachus and S. decaocto (Table S1). According to the contribution rate of environmental variables (Table 1), the dominant variables for each species are the isothermality and mean temperature of the coldest quarter for M. monachus, and the mean diurnal range and minimum temperature of the coldest month for S. decaocto, representing a cumulative contribution rate higher than 50%. From the permutation importance of bioclimatic variables (Table 1), the dominant variables for each species are the mean temperature of the coldest quarter and maximum temperature of the warmest month for M. monachus, and the mean diurnal range and annual precipitation for S. decaocto, with a cumulative contribution rate higher than 45%.
Since the first record of M. monachus, our models predicted an increase in the current habitat suitability corresponding to 71% (5642 km2) of the MCMA territorial extension (Figure 2A), and since the first record of S. decaocto, our models predicted an increase in the current habitat suitability corresponding to 69% (5489 km2) of the MCMA (Figure 2H). Currently, the highest suitable areas for both species are mainly distributed in the north, central, east, and northeastern MCMA. These high-suitability areas are buffered by medium-suitability areas, including other areas to the west, northwest, and southeast MCMA. Unsuitable areas for both species are in the southern (e.g., Cumbres del Ajusco, Chichinautzin Biological Corridor, Izta-Popo) and western (e.g., Sierra de las Cruces) mountains surrounding the MCMA (Figure 2A,H).

3.2. Distribution Patterns Under Climate Change Scenarios

The IPSL-CM6A-LR climate change scenario projections (for the year 2050) showed slight increases and decreases in the suitable habitats for the two species, principally in areas to the north, central, and eastern MCMA (Table 2, Figure 2B–G,I–N). However, under the GFDL-ESM4 climate projections, the highly suitable habitat for both species is significantly reduced by up to ~17% (Table 2, Figure 2E–G,L–N). This pattern is shown in the SSP 126 and SSP 585 scenarios (year 2050), with a slight decrease in the north and central (highest suitability), and central and western (medium suitability) peripheral areas of the MCMA for both species (Table 2, Figure 2B–G,I–N). For both species, the loss of suitable areas was slight (~24%), but steady (Figure 2B–G,I–N). The area of expansion of suitable areas for both species was less than ~800 km2 (Table 2). According to our three climate change scenarios, the area of expansion for M. monachus was 167 km2 for the 2041–2060 period (SSP 126) and 192 km2 for the 2041–2060 period (SSP 370), while the areas of contraction ranged from 156 km2 (SSP 585, IPSL-CM6A-LR) to 1369 km2 (SSP 126, GFDL-ESM4) (Table 3). In contrast, the area of expansion for S. decaocto ranged from 15 km2 (SSP 370, IPSL-CM6A-LR) to 738 km2 (SSP 585, GFDL-ESM4), while the areas of contraction ranged from 7 km2 (SSP 126, IPSL-CM6A-LR) to 721 km2 (SSP 585, GFDL-ESM4) (Table 3). Areas with stable suitability for both species were mainly located close to core zones (areas with a suitability ~ 0.75), or along discontinuous zones of suitability.
The MESS models showed different levels of dissimilarity when outside the range of the reference points (Figure 3A–N). For M. monachus, the models trained with occurrence records and projected to the MCMA showed slight climate dissimilarity in the north and west compared with those used to construct the model in the native range (Figure 3A). Under the future climate scenarios, the distribution of the climatic dissimilar area (<0, red area) in the whole potential distribution area is small (Figure 3A–G). Models for S. decaocto indicate slight climate dissimilarity in the north and central MCMA (SSP 126 in GFDL-ESM4), but this increases (SSP 585) in the western part (Figure 3H–N). Compared with the potential distribution area under the IPSL-CM6A-LR scenario in the same period in the future (Figure 3B–D,I–K), it is very noticeable in the western part, but it decreases slightly (Figure 3B–D,I–K).

3.3. Risk of Expansion into Agricultural Areas and PAs

Overall, 99.5% (3740 km2) of the current suitable habitat of M. monachus and 98% (3689 km2) of S. decaocto is within the current distribution of agricultural areas in the MCMA (Figure 4A–N). The area of suitable habitat remains stable at more than 85% for both species (Figure 4A–N). The potential suitable habitat of both species was located principally in the northern and eastern periphery of the MCMA. We identified that 80% (3711 km2) of the current suitable habitat of M. monachus is within the PAs (n = 54; 4629 km2) of the MCMA for the current period (Figure 5A–G). The area of suitable habitat remains at more than 80% for the 2041–2060 period with the IPSL-CM6A-LR climate projection (Figure 5B–D, Table S2). In contrast, the suitable area is significantly reduced to 22% in SSP 126, 46% in SSP 370, and 74% in SSP 585 under the GFDL-ESM4 climate projection scenarios (Figure 5E–G, Table S2). The potentially suitable habitats of M. monachus were located principally in PAs such as Sierra Hermosa, Zumpango lagoon, Cerro Gordo, and Sierra Pallachique (Table S2). For S. decaocto, 83% (3854 km2) of the current suitable habitat is within the PAs (n = 54; 4629 km2) for the current period (Figure 5H–N).
The area of suitable habitat remained between 64 to 84% under all scenarios (Figure 5H–N, Table S2). The potentially suitable habitats of S. decaocto were located principally in PAs such as Xochimilco, Cerro Gordo, the Cerro Ayaqueme-Huehuel volcano, Zumpango lagoon, Sierra Platachique, San Miguel Topilejo, Chichinautzin Biological Corridor, and the Tetzcotzinco system (Table S2).
A piecewise linear regression of the spread patterns in the MCMA for these species showed the presence of three relatively distinct invasion phases (Figure 6A,B). The distance from the first recorded site of invasion abruptly rose during the first year’s expansion phase from the onset (1999–2011, V = 1.45 ± 0.98 km/year for M. monachus; and 2013–2016, V = 2.67 ± 1.20 km/year for S. decaocto), with a maximum distance of 55 km in the expansion phase. The spread rates increased slightly in the second stage of the expansion phase (from 2012 to 2016 for M. monachus with V = 2.16 ± 1.01 km/year and 2017–2019 for S. decaocto with V = 3.48 ± 1.85 km/year) (Figure 6A,B), followed by a period of slow growth, with a slope not significantly different from zero (2017–2023 for M. monachus with V = 1.9 ± 0.85 km/year and 2020–2023 for S. decaocto with V = 2.28 ± 1.32 km/year), possibly representing a plateau (or saturation) phase (Figure 6A,B).

4. Discussion

Several studies over recent years have documented how the climate and land use change, and human transportation may progressively remove physiological constraints for the establishment and spread of some invasive species [103,104,105], facilitating their expansion into formerly limiting regions [35,106,107,108,109]. These processes may be accelerated via either anthropic activities or natural phenomena [10,110,111]. The model predictions for M. monachus and S. decaocto suggest that their current distributions in the invaded ranges may be largely explained either by the rapid incorporation of these species into the local culture due to their use as pets [10,14], which has probably promoted the acclimatization of these invasive species beyond their original climatic thresholds [112], or by favorable local environmental conditions [73], as is supported by previous studies demonstrating a close association of these species with human-modified habitats in North America and Europe [10,28,113,114].
Our prediction models showed that the distributions of both invasive species remain stable with slight increases (~2%), but with decreases up to 17% according to the different scenarios for the two species. The predicted invasion risk in both invasive species exhibited approximately similar trends primarily in the north, west, and central parts of the MCMA in the current and future scenarios. Our models for M. monachus showed a range increase to the periphery of the MCMA, where suitable conditions are found in greenspaces with non-native vegetation (e.g., Eucalyptus spp., Phoenix canariensis, Washingtonia robusta), and toward rural communities dedicated to grain agriculture in the case of S. decaocto, suggesting these habitats as favorable for the establishment of these species. These results also support previous findings suggesting that these species may be limited to human-altered habitats in newly invaded areas [107,108,109,115]. However, some studies have suggested that S. decaocto tends to avoid areas of intensive agriculture and conserved forest [28,116,117].
In Europe, the distribution of M. monachus and S. decaocto are generally restricted to relatively warmer regions, although both species have also been found in temperate areas [28,113,118,119]. Romagosa and McEneaney [8] and Ingeloff et al. [73] mentioned that these species have successfully colonized tropical, subtropical, temperate, and arid areas in North America and the Caribbean region, which suggests that these species may track environmental conditions similar to those in their original distribution area in southern Asia [21,22]. These findings suggest that in Mediterranean Europe, individuals may come from local breeding populations, reflecting a process of a posteriori population establishment (i.e., when a small group of individuals separate from the main population to form a new colony), as shown in other invasive species [10,113,114,120,121].
Hiley et al. [122] cautioned that while PAs may not facilitate the colonization of invasive species at their initial stage, they are more susceptible to invasion and an exponential population increase. Although our results showed that both species may be in a saturation phase (or plateau), these invasive bird species represent a risk to conserved environments around the MCMA due to their rapid spread, which may be enhanced through the growth of the urban area and reforestation within the PAs with exotic trees that facilitate their establishment. Local studies may support this process in PAs in different regions of Mexico [109,115,123,124].
Some invasive alien species may remain in a quiescence state, referred to as a lag phase [125,126], after their introductions, occupying a small area for several years before rapidly expanding. However, the populations of M. monachus and S. decaocto in invaded areas have grown exponentially over short time periods [10,127,128,129]. For example, S. decaocto has experienced three phases during its colonization and establishment in North America: (1) a population exponential increase, (2) a leveling at or above the local environment carrying capacity, and (3) an a posteriori decrease [130]. An initial lag phase, associated with the first population establishment of these species in the MCMA but with limited spatial expansion, was not clearly visible from our data; it may have taken place before the first species record, or may have been very short, or it is unclearly defined because of multiple intentional or unintentional releases, as these birds are maintained as pets. However, it would be necessary to consider whether other factors, such as a low abundance, a death rate exceeding the birth rate, and competition between native species or with other already established invasive species, may have been overcome. This may suggest that the populations of these species routinely overshoot the carrying capacity of local habitats. Our results suggest that the relatively recent records in central Mexico present this pattern, suggesting that these species have been expanding their range distribution in the second stage of the expansion phase, but still have not yet reached the a posteriori decrease stage.
From this perspective, the distribution expansion of M. monachus and S. decaocto in central Mexico may be largely attributed to their propensity to move and occupy suitable conditions in contiguous areas favoring their establishment [11,14,113,116]. However, M. monachus and S. decaocto can presumably “jump” larger distances if suitable conditions are not available at close ranges, thus starting the colonization process, which may be followed by a process of establishment, or it may be that they have been released in nearby areas and thus simulate long-distance dispersal [73,113,118].
Our models support a slight increase in the distribution of M. monachus and S. decaocto in the invaded regions, suggesting a lack of acclimatization to changing environments, and thus decreasing the significance of pets being released. In this sense, our results are largely consistent with those of previous studies [131,132,133], in which a suitable habitat range will slightly increase as global warming intensity increases under low-level SSPs, but in which excessive temperature increases might restrict or decrease the habitat suitability [134,135].
Future projections of our models for both species showed that the climatic suitability remains relatively stable but presents considerable decreases up to 24% for M. monachus and up to 17% for S. decaocto [see Table 3]. This pattern is congruent with invasive phases, where decreases noted in areas of climatic suitability are possibly related to a plateau or saturation phase, after an increase that seems related to the second stage of the expansion phase. Then, the expansion phase flows into a saturation phase with extremely low spread rates and a lower (but still increasing) occupancy range because of biological constraints and/or the filling of suitable niche environments.

5. Conclusions

Invasive alien species may have major negative impacts on native communities and promote the homogenization of flora and fauna [36,136,137,138]. However, although M. monachus and S. decaocto are both listed as a highly invasive species [139,140], they are still being traded as pets in central Mexico, as well as in other regions of the country. However, although the number of established populations is apparently still small, and the potential damage is still unknown [141], timely and efficient monitoring is necessary to apply rapid and effective actions to contain or eradicate these invasive alien species. Otherwise, in the short term, these species may potentially colonize sites with limited anthropic influence [142,143], such as PAs [103,144]. Given that population dynamics and the invasive potential of these species in Mexico are still largely unknown, it is essential that government agencies finance and promote further scientific research to establish the effect of its presence in Mexico. Therefore, an integrated invasive species management technique should focus on eradication or population reduction (e.g., using toxicants, shooting, and trapping) to keep populations at levels where nonlethal tools can be utilized to reduce damage. However, the efficacy of an eradication campaign depends on biological, environmental, and economic factors, along with the issuance of a social license for lethal removal [145]. In addition, environmental education programs and dissemination via different media are required, regarding the danger posed by the introduction of invasive alien species and the impacts these invasive species may have on the biota and native ecosystems.
Although biological invasions are now better understood from an ecological perspective, a clearly less studied issue is the human dimension. Human activities could artificially expand the geographical range [146,147] of M. monachus and S. decaocto. It is possible that range expansion has been promoted due to a population increase in towns surrounded by agricultural areas, which benefited its presence, likely due to stored food sources [10,28,116,117,127].
Climate niche models are highly important tools in the study of prevention and control of biological invasions [148,149,150], but their application requires accurate predictions on the potential spatial extent of these invasions, as this facilitates concentrating economic and research efforts aimed at preventing the arrival of invasive species. In addition, potential distribution and climate niche models of invasive species also provide new opportunities to analyze whether their expansion depends on environmental conditions or human activities.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/su16167071/s1. Table S1, Areas of calibration and performance statistics for Myiopsitta monachus and Streptopelia decaocto models; Table S2, Suitable areas of Myiopsitta monachus and Streptopelia decaocto in protected areas within the MCMA under climate change scenarios.

Author Contributions

J.E.R.-A. conceived the study and performed the data compilation. J.E.R.-A. and D.A.P.-T. designed and performed the analyses (including the species distribution modeling). All authors help with the interpretation of results and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data are available upon request.

Acknowledgments

Thanks to the curators of the multiple institutions worldwide who have supplied data, access to collections, and invaluable logistic support. DAP-T appreciate the financial support provided by the Programa de Investigación en Cambio Climático (PINCC-UNAM).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. International Union for Conservation Nature (IUCN). Invasive Alien Species and Climate Change; IUCN: Gland, Switzerland, 2022. [Google Scholar]
  2. Millennium Ecosystem Assessment. Ecosystems and Human Well-Being: Biodiversity Synthesis; World Resources Institute: Washington, DC, USA, 2005. [Google Scholar]
  3. Catford, J.A.; Bode, M.; Tilman, D. Introduced species that overcome life history tradeoffs can cause native extinctions. Nat. Commun. 2018, 9, 2131. [Google Scholar] [CrossRef] [PubMed]
  4. Hulme, P.E.; Pyšek, P.; Nentwig, W.; Vilà, M. Will threat of biological invasions unite the European Union? Science 2009, 324, 40–41. [Google Scholar] [CrossRef] [PubMed]
  5. International Union for Conservation Nature (IUCN). Guidelines for the Prevention of Biodiversity Loss due to Biological Invasion; IUCN: Gland, Switzerland, 2000. [Google Scholar]
  6. Richardson, D.M. Fifty Years of Invasion Ecology: The Legacy of Charles Elton; Blackwell Publishing Ltd.: Oxford, UK, 2010. [Google Scholar]
  7. Pârâu, L.G.; Strubbe, D.; Mori, E.; Menchetti, M.; Ancillotto, L.; van Kleunen, A.; White, R.L.; Luna, Á.; Hernández-Brito, D.; Le Louarn, M.; et al. Rose-ringed parakeet Psittacula krameria populations and numbers in Europe: A complete overview. Open Ornithol. J. 2016, 9, 1–13. [Google Scholar] [CrossRef]
  8. Romagosa, C.M.; McEneaney, T. Eurasian Collared-Dove in North America and the Caribbean. N. Am. Birds 1999, 53, 348–353. [Google Scholar]
  9. Muñoz, A.R.; Real, R. Assessing the potential range expansion of the exotic Monk Parakeet in Spain. Divers. Distrib. 2006, 12, 656–665. [Google Scholar] [CrossRef]
  10. Fujisaki, I.; Pearlstine, E.V.; Mazzotti, F.J. The rapid spread of invasive Eurasian Collared-Doves Streptopelia decaocto in the continental USA follows human-altered habitats. Ibis 2010, 152, 622–632. [Google Scholar] [CrossRef]
  11. Rodríguez-Pastor, R.; Señar, J.C.; Ortega, A.; Faus, J.; Uribe, F.; Montalvo, T. Distribution patterns of invasive Monk parakeets (Myiopsitta monachus) in an urban habitat. Anim. Biodivers. Conserv. 2012, 35, 107–117. [Google Scholar] [CrossRef]
  12. Mori, E.; di Febbraro, M.; Foresta, M.; Melis, P.; Romanazzi, E.; Notari, A.; Boggiano, F. Assessment of the current distribution of free-living parrots and parakeets (Aves: Psittaciformes) in Italy: A synthesis of published data and new records. Ital. J. Zool. 2013, 80, 158–167. [Google Scholar] [CrossRef]
  13. Viana, I.R.; Strubbe, D.; Zocche, J.J. Monk parakeet invasion success: A role for nest thermoregulation and bactericidal potential of plant nest material? Biol. Invasions 2016, 18, 1305–1315. [Google Scholar] [CrossRef]
  14. Romagosa, C.; Labiski, R. Establishment and dispersal of the Eurasian Collared-Dove in Florida. J. Field Ornithol. 2000, 71, 159–166. [Google Scholar] [CrossRef]
  15. MacGregor-Fors, I.; Calderón-Parra, R.; Meléndez-Herrada, A.; López-López, S.; Schondube, J.E. Pretty, but dangerous! Records of non-native Monk Parakeets (Myiopsitta monachus) in Mexico. Rev. Mex. Biodivers. 2011, 81, 1053–1056. [Google Scholar] [CrossRef]
  16. Terregino, C.; Cattoli, G.; Grossele, B.; Bertoli, E.; Tisato, E.; Capua, I. Characterization of Newcastle disease virus isolates obtained from Eurasian-Collared Doves (Streptopelia decaocto) in Italy. Avian Pathol. 2003, 32, 63–68. [Google Scholar] [CrossRef]
  17. Kubicek, O.; Taras, L. Incidence of pigeon circovirus in Eurasian Collared-Dove (Streptopelia decaocto) detected by nesterted PCR. Acta Vet. Brno 2005, 74, 361–368. [Google Scholar] [CrossRef]
  18. Briceño, C.; Surot, D.; González-Acuña, D.; Martínez, F.J.; Fredes, F. Parasitic survey on introduced monk parakeets (Myiopsitta monachus) in Santiago, Chile. Rev. Bras. Parasitol. Veterinária 2017, 2961, 129–135. [Google Scholar] [CrossRef] [PubMed]
  19. Del Hoyo, J.; Elliot, A.; Sargatal, J. Handbook of the Birds of the World; Lynx, Ed.; Sandgrouse to Cuckoos: Barcelona, Spain, 1997; Volume 4. [Google Scholar]
  20. BirdLife International. Myiopsitta monachus. The IUCN Red List of Threatened Species. E.T45427277A1321898. 2018. Available online: https://www.iucnredlist.org/species/45427277/132189848 (accessed on 2 March 2024).
  21. Baptista, L.F.; Trail, P.W.; Horbit, H.M. Eurasian Collared-Dove (Streptopelia decaocto). In Handbook of the Birds of the World; Lynx, Del Hoyo, J., Elliot, A., Sargatal, J., Eds.; Lynx: Barcelona, Spain, 1997; Volume 4. [Google Scholar]
  22. BirdLife International. Streptopelia decaocto. The IUCN Red List of Threatened Species. E.T22727811A154457. 2019. Available online: https://www.iucnredlist.org/species/22727811/154457750 (accessed on 2 March 2024).
  23. Álvarez-Romero, J.G.; Medellín, R.A.; Oliveras de Ita, A.; Gómez de Silva, H.; Sánchez, O. Animales Exóticos en México: Una Amenaza para la Biodiversidad; Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, Instituto de Ecología-UNAM, Secretaria de Medio Ambiente y Recursos Naturales: Ciudad de México, México, 2008. [Google Scholar]
  24. eBird. Myiopsitta monachus. Audubon-The Cornel Lab of Ornithology. Available online: https://ebird.org/map/monpar?neg=true&env.minX=&env.minY=&env.maxX=&env.maxY=&zh=false&gp=false&ev=Z&excludeExX=false&excludeExAll=false&mr=1-12&bmo=1&emo=12&yr=all&byr=1900&eyr=2024 (accessed on 7 September 2023).
  25. eBird. Streptopelia decaocto. Audubon-The Cornel Lab of Ornithology. Available online: https://ebird.org/map/eucdov?neg=true&env.minX=&env.minY=&env.maxX=&env.maxY=&zh=false&gp=false&ev=Z&excludeExX=false&excludeExAll=false&mr=1-12&bmo=1&emo=12&yr=all&byr=1900&eyr=2024 (accessed on 7 September 2023).
  26. Rocha-Camarero, G.; de Trucios, S.J.H. The spread of the Collared Dove Streptopelia decaocto in Europe: Colonization patterns in the west of the Iberian Peninsula. Bird Study 2002, 49, 11–16. [Google Scholar] [CrossRef]
  27. Eraud, C.; Boutin, J.-M.; Roux, D.; Faivre, B. Spatial dynamics of an invasive bird species assessed using robust design occupancy analysis: The case of the Eurasian Collared-Dove (Streptopelia decaocto) in France. J. Biogeogr. 2007, 34, 1077–1086. [Google Scholar] [CrossRef]
  28. Eraud, C.; Jacquet, A.; Legagneux, P. Post-fledging movements, home range, and survival of juvenile Eurasian Collared-doves in western France. Condor 2011, 113, 150–158. [Google Scholar] [CrossRef]
  29. Burger, J.; Gochfeld, M. Exotic monk parakeets (Myiopsitta monachus) in New Jersey: Nest site selection, rebuilding following removal, and their urban wildlife appeal. Urban Ecosyst. 2009, 12, 185–196. [Google Scholar] [CrossRef]
  30. South, J.; Pruett-Jones, S. Patterns of flock size, diet, and vigilance of naturalized Monk Parakeets in Hyde Park, Chicago. Condor 2000, 102, 848. [Google Scholar] [CrossRef]
  31. Domènech, J.; Carrillo, J.; Senar, J.C. Population size of the Monk Parakeet (Myiopsitta monachus) in Catalonia. Rev. Catalana D’ornitologia 2003, 20, 1–9. [Google Scholar]
  32. Secondi, J.; Hartog, P.M.; Cate, C. To trill or not to trill? Territorial response to heterospecific vocal trait in male collared doves, Streptopelia decaocto. Behav. Ecol. 2013, 14, 694–701. [Google Scholar] [CrossRef]
  33. Bled, F.; Royle, J.A.; Cam, E. Hierarchical modeling of an invasive spread: The Eurasian Collared-Dove Streptopelia decaocto in the United States. Ecol. Appl. 2011, 21, 290–302. [Google Scholar] [CrossRef] [PubMed]
  34. Battisti, C.; Zullo, F. A recent colonizer bird as indicator of human-induced landscape change: Eurasian Collared-Dove (Streptopelia decaocto) in a small Mediterranean island. Reg. Environ. Chang. 2019, 19, 2113–2121. [Google Scholar] [CrossRef]
  35. Castro, J.; Sáez, C.; Molina-Morales, M. The Monk parakeet (Myiopsitta monachus) as a potential pest for agriculture in the Mediterranean basin. Biol. Invasions 2022, 24, 895–903. [Google Scholar] [CrossRef]
  36. McKinney, M.L.; Lockwood, J.L. Biotic homogenization: A few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 1999, 14, 450–453. [Google Scholar] [CrossRef]
  37. McKinney, M.L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 2006, 127, 247–260. [Google Scholar] [CrossRef]
  38. Levine, J.; Vilà, M.; Antonio, C.; Dukes, J.; Grigulis, K.; Lavorel, S. Mechanisms underlying the impacts of exotic plant invasions. Proc. R. Soc. Lond. Ser. B Biol. Sci. 2003, 270, 775–781. [Google Scholar] [CrossRef] [PubMed]
  39. Oliveras, J.; Bas, J.M.; Gómez, C. Reduction of the ant mandible gap range after biotic homogenization caused by an ant invasion (Hymenoptera: Formicidae). Sociobiology 2005, 45, 829–838. [Google Scholar]
  40. Hansen, A.K.; Ortega, Y.K.; Six, D.L. Comparison of ground beetle (Coleoptera: Carabidae) assemblages in rocky mountain savannas invaded and un-invaded by an exotic forb, spotted knapweed. Northwest Sci. 2009, 83, 348–360. [Google Scholar] [CrossRef]
  41. Li, F.L.; Li, M.G.; Zan, Q.J.; Zhang, W.Y.; Wu, Z.; Wang, Y.J. Effects of the residues of Cuscuta campestris and Mikania micrantha on subsequent plant germination and early growth. J. Integr. Agric. 2012, 11, 1852–1860. [Google Scholar] [CrossRef]
  42. Olden, J.D.; LeRoy-Poff, N.; Douglas, M.R.; Douglas, M.E.; Fausch, K.D. Ecological and evolutionary consequences of biotic homogenization. Trends Ecol. Evol. 2004, 19, 18–24. [Google Scholar] [CrossRef]
  43. Guisan, A.; Thuiller, W. Predicting species distribution: Offering more than simple habitat models. Ecol. Lett. 2005, 8, 993–1009. [Google Scholar] [CrossRef] [PubMed]
  44. Warren, D.L.; Matzke, N.J.; Iglesias, T.L. Evaluating presence-only species distribution models with discrimination accuracy is uninformative for many applications. J. Biogeogr. 2020, 47, 167–180. [Google Scholar] [CrossRef]
  45. Wiens, J.A.; Stralberg, D.; Jongsomjit, D.; Howell, C.A.; Snyder, M.A. Niches, models, and climate change: Assessing the assumptions and uncertainties. Proc. Natl. Acad. Sci. USA 2009, 106, 19729–19736. [Google Scholar] [CrossRef] [PubMed]
  46. Suarez, S.; Puyol, R.; Schafer, C.; Mucklich, F. Ecological Niches and Geographic Distributions; Monographs in Population Biology 49; Princeton University Press: Princeton, NJ, USA, 2011. [Google Scholar] [CrossRef]
  47. Fernandes, R.F.; Honrado, J.P.; Guisan, A.; Raxo, A.; Alves, P.; Martins, J.; Vicente, J.R. Species distribution models support the need of international cooperation towards successful management of plant invasion. J. Nat. Conserv. 2019, 49, 85–94. [Google Scholar] [CrossRef]
  48. Thompson, B.K.; Olden, J.D.; Converse, S.J. Mechanistic invasive species management models and their application in conservation. Conserv. Sci. Pract. 2021, 3, e533. [Google Scholar] [CrossRef]
  49. Nunes, A.L.; Tricarico, E.; Panov, V.E.; Cardoso, A.C.; Katsanevakis, S. Pathways and gateways of freshwater invasions in Europe. Aquat. Invasions 2015, 10, 359–370. [Google Scholar] [CrossRef]
  50. Wan, J.-Z.; Wang, C.-J.; Tan, J.-F.; Yu, F.-H. Climatic niche divergence and habitat suitability of eight alien invasive weeds in China under climate change. Ecol. Evol. 2017, 7, 1541–1552. [Google Scholar] [CrossRef]
  51. Bonter, D.N.; Zuckerberg, B.; Dickinson, J.L. Invasive birds in a novel landscape: Habitat associations and effects on established species. Ecography 2010, 33, 494–502. [Google Scholar] [CrossRef]
  52. Guisan, A.; Tingley, R.; Baumgartner, J.B.; Naujokaitis-Lewis, I.; Sutcliffe, P.R.; Tulloch, A.I.T.; Regan, T.J.; Brotons, L.; Mcdonald-Madden, E.; Mantyka-Pringle, C.; et al. Predicting species distributions for conservation decisions. Ecol. Lett. 2013, 16, 1424–1435. [Google Scholar] [CrossRef]
  53. Gallien, L.; Carboni, M. The community ecology of invasive species: Where are we and what´s next? Ecography 2017, 40, 335–352. [Google Scholar] [CrossRef]
  54. United Nations. UN World Urbanization Prospects 2018. Available online: https://population.un.org/wup/ (accessed on 4 March 2024).
  55. Grimm, N.B.; Faeth, S.H.; Golubiewski, N.E.; Redman, C.L.; Wu, J.; Bai, X.; Briggs, J.M. Global change and the ecology of cities. Science 2008, 319, 756–760. [Google Scholar] [CrossRef] [PubMed]
  56. INEGI. Anuario Estadístico y Geográfico por Entidad Federativa 2022; INEGI: Ciudad de México, México, 2023.
  57. Reygadas, D. Delimitación del área de estudio y regionalización. In La Biodiversidad en la Ciudad de México; CONABIO-SEDEMA, coord.: Ciudad de México, México, 2016; Volume I, pp. 30–35. [Google Scholar]
  58. Sorani, V.; Rodríguez, G.; Reygadas, D. Usos y cobertura de suelo. In La Biodiversidad de la Ciudad de México; CONABIO-SEDEMA, coord.: Ciudad de México, México, 2016; Volume I, pp. 104–112. [Google Scholar]
  59. Navarro-Sigüenza, A.G.; Peterson, A.T.; Gordillo-Martínez, A. Museums working together: The atlas of the birds of Mexico. Bull. Br. Ornithol. Club 2003, 123, 207–225. [Google Scholar]
  60. Global Biodiversity Information Facility (GBIF). Myiopsitta monachus in GBIF Secretariat. GBIF Occurrence Download. Available online: https://www.gbif.org/occurrence/download/0009127-230828120925497 (accessed on 8 September 2023). [CrossRef]
  61. Global Biodiversity Information Facility (GBIF). Streptopelia decaocto in GBIF Secretariat. GBIF Occurrence Download. Available online: https://www.gbif.org/occurrence/download/0009129-230828120925497 (accessed on 8 September 2023). [CrossRef]
  62. Dyer, E.E.; Redding, D.W.; Blackburn, T.M. The global avian invasion atlas, a database of alien bird distributions worldwide. Sci. Data 2017, 4, 170041. [Google Scholar] [CrossRef]
  63. Aiello-Lammens, M.E.; Boria, R.A.; Radosavljevic, A.; Vilela, B.; Anderson, R.P. spThin: A R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 2015, 38, 541–545. [Google Scholar] [CrossRef]
  64. RStudio Team. RStudio: Integrated Development for R. Rstudio; PBC: Boston, MA, USA, 2023; Available online: http://www.rstudio.com/ (accessed on 23 June 2024).
  65. Brown, J.L. SDM toolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 2014, 5, 694–700. [Google Scholar] [CrossRef]
  66. ESRI. ArcGIS for Desktop 10.2. 2014. Available online: https://www.esri.com (accessed on 23 June 2024).
  67. Kozak, K.H.; Wiens, J.J. Does niche conservatism promote speciation? A case study in North American salamanders. Evolution 2006, 60, 2604–2621. [Google Scholar] [CrossRef]
  68. Rödder, D.; Engler, J.O. Quantitative metrics of overlaps in Grinnellian niches: Advances and possible drawbacks. Glob. Ecol. Biogeogr. 2011, 20, 915–927. [Google Scholar] [CrossRef]
  69. Karger, D.N.; Conrad, O.; Böhner, J.; Kawohl, T.; Kreft, H.; Soria-Auza, R.W.; Zimmermann, N.E.; Linder, H.P.; Kessler, M. Climatologies at High Resolution for the Earth’s Land Surface Areas. Sci. Data 2017, 4, 170122. [Google Scholar] [CrossRef]
  70. Naimi, B. usdm: Uncertainty Analysis for Species Distribution Models. R Package v. 1. 2015. Available online: https://cran.r-project.org/web/packages/usdm/ (accessed on 23 June 2024).
  71. Naimi, B.; Araújo, M.B. sdm: A reproducible and extensible R platform for species distribution modelling. Ecography 2016, 39, 368–375. [Google Scholar] [CrossRef]
  72. Cobos, M.E.; Peterson, A.T.; Barve, N.; Osorio-Olvera, L. kuenm: An R package for detailed development of ecological niche models using Maxent. Peer J. 2019, 7, e6281. [Google Scholar] [CrossRef]
  73. Ingenloff, K.; Hensz, C.M.; Anamza, T.; Barve, V.; Campbell, L.P.; Cooper, J.C.; Komp, E.; Jimenez, L.; Olson, K.V.; Osorio-Olvera, L.; et al. Predictable invasion dynamics in North American populations of the Eurasian Collared-Dove Streptopelia decaocto. Proc. R. Soc. Lond. B 2017, 284, 20171157. [Google Scholar] [CrossRef]
  74. Bohl, C.L.; Kass, J.M.; Anderson, R.P. A new null model approach to quantify performance and significance for ecological niche models of species distributions. J. Biogeogr. 2019, 46, 1101–1111. [Google Scholar] [CrossRef]
  75. Intergovernmental Panel on Climate Change (IPCC). Summary for Policymakers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gornis, M.I., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2021; pp. 3–32. [Google Scholar] [CrossRef]
  76. Pirani, A.; Fuglestvedt, J.S.; Byers, E.; O’neill, B.; Riahi, K.; Lee, J.-Y.; Marotzke, J.; Rose, S.K.; Schaeffer, R.; Tebaldi, C. Scenarios in IPCC assessments: Lessons from AR6 and opportunities for AR7. npj Clim. Action 2024, 3, 1. [Google Scholar] [CrossRef]
  77. Tebaldi, C.; Debeire, K.; Eyring, V.; Fischer, E.; Fyfe, J.; Friedlingstein, P.; Knutti, R.; Lowe, J.; O’Neill, B.; Sanderson, B.; et al. Climate model projections from the scenario model intercomparison project (scenario MIP) of CMIP6. Earth Syst. Dyn. 2021, 12, 253–293. [Google Scholar] [CrossRef]
  78. Tang, J.; Zhao, X. Forecasting the combined effects of future climate and land use change on the suitable habitat of Davidia involucrate Baill. Ecol. Evol. 2022, 12, e9023. [Google Scholar] [CrossRef] [PubMed]
  79. Harris, G.M.; Sesnie, S.E.; Stewart, D.R. Climate change and ecosystem shifts in the southwestern United States. Sci. Rep. 2023, 13, 19964. [Google Scholar] [CrossRef]
  80. Laporta, G.Z.; Potter, A.M.; Oliveira, J.F.A.; Bourke, B.P.; Pecor, D.B.; Linton, Y.-M. Global distribution of Aedes aegypti and Aedes albopictus in a climate change scenario of regional rivalry. Insects 2023, 14, 49. [Google Scholar] [CrossRef]
  81. Elith, J.; Phillips, S.J.; Hastie, T.; Dudík, M.; Chee, Y.E.; Yates, C.J. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 2011, 17, 43–57. [Google Scholar] [CrossRef]
  82. Phillips, S.J.; Anderson, R.P.; Schapire, R.E. Maximum entropy modelling of species geographic distributions. Ecol. Model. 2006, 190, 231–259. [Google Scholar] [CrossRef]
  83. Graham, C.H.; Elith, J.; Hijmans, R.J.; Guisan, A.; Peterson, A.T.; A Loiselle, B.; The Nceas Predicting Species Distributions Working Group. The influence of spatial errors in species occurrence data used in distribution models. J. Appl. Ecol. 2008, 45, 239–247. [Google Scholar] [CrossRef]
  84. Merow, C.; Smith, M.J.; Edwards, T.C., Jr.; Guisan, A.; McMahon, S.M.; Normand, S.; Wilfried Thuiller, W.; Wüest, R.O.; Zimmermann, N.E.; Elith, J. What do we gain from simplicity versus complexity in species distribution models? Ecography 2014, 37, 1267–1281. [Google Scholar] [CrossRef]
  85. Warren, D.L.; Seifert, S.N. Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecol. Appl. 2011, 21, 335–342. [Google Scholar] [CrossRef] [PubMed]
  86. Brewer, M.J.; Butler, A.; Cooksley, S.L. The relative performance of AIC, AICC and BIC in the presence of unobserved heterogeneity. Methods Ecol. Evol. 2016, 7, 679–692. [Google Scholar] [CrossRef]
  87. Ashraf, U.; Chaudhry, M.N.; Peterson, A.T. Ecological niche models of biotic interactions predict increasing pest risk to olive cultivars with changing climate. Ecosphere 2012, 12, e03714. [Google Scholar] [CrossRef]
  88. Anderson, R.P.; Lew, D.; Peterson, A.T. Evaluating predictive models of species’ distributions: Criteria for selecting optimal models. Ecol. Model. 2003, 162, 211–232. [Google Scholar] [CrossRef]
  89. Fielding, A.H.; Bell, J.F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 1997, 24, 38–49. [Google Scholar] [CrossRef]
  90. Elith, J.H.; Graham, C.P.H.; Anderson, R.P.; Dudík, M.; Ferrier, S.; Guisan, A.; Hijmans, R.J.; Huettmann, F.; Leathwick, J.R.; Lehmann, A.; et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 2006, 29, 129–151. [Google Scholar] [CrossRef]
  91. Peterson, A.T.; Papeş, M.; Soberón, J. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol. Model. 2008, 213, 63–72. [Google Scholar] [CrossRef]
  92. Allouche, O.; Tsoar, A.; Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 2006, 43, 1223–1232. [Google Scholar] [CrossRef]
  93. Wenger, S.J.; Olden, J.D. Assessing transferability of ecological models: An underappreciated aspect of statistical validation. Methods Ecol. Evol. 2012, 3, 260–267. [Google Scholar] [CrossRef]
  94. Yates, K.L.; Bouchet, P.J.; Caley, M.J.; Mengersen, K.; Randin, C.F.; Parnell, S.; Fielding, A.H.; Bamford, A.J.; Ban, S.; Barbosa, A.M.; et al. Outstanding challenges in the transferability of ecological models. Trends Ecol. Evol. 2018, 33, 790–802. [Google Scholar] [CrossRef] [PubMed]
  95. Elith, J.; Kearney, M.; Phillips, S. The art of modelling range-shifting species. Methods Ecol. Evol. 2010, 1, 330–342. [Google Scholar] [CrossRef]
  96. Alkishe, A.A.; Peterson, A.T.; Samy, A.M. Climate change influences on the potential geographic distribution of the disease vector tick Ixodes Ricinus. PLoS ONE 2017, 12, e0189092. [Google Scholar] [CrossRef] [PubMed]
  97. Xin, F.; Liu, J.; Chang, C.; Wang, Y.; Jia, L. Evaluating the influence of climate change on Sophora moorcroftiana (Benth.) Baker. Habitat distribution on the Tibetan Plateau using maximum entropy model. Forest 2021, 12, 1230. [Google Scholar] [CrossRef]
  98. Liu, C.; White, M.; Newell, G. Selection thresholds for the prediction of species occurrence with presence-only data. J. Biogeogr. 2013, 40, 778–789. [Google Scholar] [CrossRef]
  99. Gilbert, M.; Liebhold, A. Comparing methods for measuring the rate of spread of invading populations. Ecography 2010, 33, 809–817. [Google Scholar] [CrossRef]
  100. Aikio, S.; Duncan, R.P.; Hulme, P.E. Lag-phases in alien plant invasions: Separating the facts from the artefacts. Oikos 2010, 119, 370–378. [Google Scholar] [CrossRef]
  101. Martelli, F.; Paradiso, F.; Ghidotti, S.; Viterbi, R.; Cerrato, C.; Bonelli, S. Invasion patterns and niche comparison of the butterfly Cacyreus marshelli among native and non-native range. Biol. Invasion 2022, 24, 3935–3952. [Google Scholar] [CrossRef]
  102. Davies, R.B. Hypothesis testing when an invasive parameter is present only under the alternative. Biometrika 1987, 74, 33–43. [Google Scholar]
  103. Gallardo, B.; Gallardo, B.; Gallardo, B.; Aldridge, D.C.; Aldridge, D.C.; Aldridge, D.C.; González-Moreno, P.; González-Moreno, P.; González-Moreno, P.; Pergl, J.; et al. Protected areas offer refuge from invasive species spreading under climate change. Glob. Chang. Biol. 2017, 23, 5331–5343. [Google Scholar] [CrossRef] [PubMed]
  104. Beaury, E.M.; Fusco, E.J.; Jackson, M.R.; Laginhas, B.B.; Morelli, T.L.; Allen, J.M.; Pasquarella, V.J.; Bradley, B.A. Incorporating climate change into invasive species management: Insights from managers. Biol. Invasions 2020, 22, 233–252. [Google Scholar] [CrossRef]
  105. Rai, P.K.; Singh, J.S. Invasive alien species: Their impact on environment, ecosystem services and human health. Ecol. Indic. 2020, 111, 106020. [Google Scholar] [CrossRef]
  106. Walther, G.-R.; Roques, A.; Hulme, P.E.; Sykes, M.T.; Pyšek, P.; Kühn, I.; Zobel, M.; Bacher, S.; Botta-Dukát, Z.; Bugmann, H.; et al. Alien species in a warmer world: Risks and opportunities. Trends Ecol. Evol. 2009, 24, 686–693. [Google Scholar] [CrossRef] [PubMed]
  107. Senar, J.C.; Domènech, J.; Arroyo, L.; Torre, I.; Gordo, O. An evaluation of Monk parakeet damage to crops in the metropolitan area of Barcelona. Anim. Biodivers. Conserv. 2016, 39, 141–145. [Google Scholar] [CrossRef]
  108. Ceja-Madrigal, A.; Pacheco-Muñoz, R.; Navarro-Sigüenza, A.G.; Rodríguez, P.; Jiménez-Cruz, M.; Schondube, J.E. Factors affecting invasion process of a megadiverse country by two exotic bird species. Anthropocene 2023, 43, 100399. [Google Scholar] [CrossRef]
  109. Huerta-Sánchez, S.A.; Huerta-Martínez, C.; Muñoz-Urias, A.; Neri-Luna, C.; Sahagún-Sánchez, F.J.; Castruita-Domínguez, J.P. Evaluation of the invasion potential of the Monk parakeet, Myiopsitta monachus, in Natural Protected Areas in Mexico. Anim. Biodivers. Conserv. 2023, 46, 13–23. [Google Scholar] [CrossRef]
  110. Bellingham, P.J.; Tanner, E.V.J.; Healey, J.R. Hurricane disturbance accelerates invasion by the alien tree Pittosporum undulatum in Jamaica montane rain forests. J. Veg. Sci. 2005, 16, 675–684. [Google Scholar] [CrossRef]
  111. Shiels, A.B.; Lombard, C.D.; Shiels, L.; Hillis-Starr, Z. Invasive rat establishment and change in small mammal populations on Caribbean Islands following two hurricanes. Glob. Ecol. Conserv. 2020, 22, e00986. [Google Scholar] [CrossRef]
  112. Sax, D.F.; Early, R.; Bellemare, J. Niche syndromes, species extinction risks, and management under climate change. Trends Ecol. Evol. 2013, 28, 517–523. [Google Scholar] [CrossRef]
  113. Postigo, J.L.; Strubbe, D.; Mori, E.; Ancillotto, L.; Carneiro, I.; Latsoudis, P.; Menchetti, M.; Pârâu, L.G.; Parrott, D.; Reino, L.; et al. Mediterranean versus Atlantic monk parakeets Myiopsitta monachus: Towards differentiated management at the European scale. Pest Manag. Sci. 2019, 75, 915–922. [Google Scholar] [CrossRef] [PubMed]
  114. Hernández-Brito, D.; Carrete, M.; Tella, J.L. Annual censuses and citizen science data show rapid population increases and range expansion of invasive Rose-ringed and Monk parakeets in Seville, Spain. Animals 2022, 12, 677. [Google Scholar] [CrossRef] [PubMed]
  115. Camacho-Cervantes, M.; Schondube, J.E. Habitat use by the invasive exotic Eurasian Collared-Dove (Streptopelia decaocto) and native dove species in the Chamela-Cuixmala region of West Mexico. Wilson J. Ornithol. 2018, 130, 902–907. [Google Scholar] [CrossRef]
  116. Hudson, R. Collared Doves in Britain and Ireland during 1965–1970. Br. Birds 1972, 65, 139–155. [Google Scholar]
  117. Tinajero, R.; Rodríguez-Estrella, R. Incremento en la distribución y primer registro de anidación de la paloma de collar (Streptopelia decaocto) en la península de Baja California, México. Rev. Mex. Biodivers. 2014, 85, 898–909. [Google Scholar] [CrossRef]
  118. Postigo, J.L.; Schwartz, A.; Strubbe, D.; Roman-Muñoz, A. Unrelenting spread of the alien monk parakeet Myiopsitta monachus in Israel. Is it time to sound the alarm? Pest Manag. Sci. 2017, 73, 349–353. [Google Scholar] [CrossRef] [PubMed]
  119. Bermúdez-Cavero, A.O.; Bernât-Ponce, E.; Gil-Delgado, J.A.; López-Iborra, G.M. Urban landscape selection by Eurasian collred dove (Streptopelia decaocto) in eastern Spain. Caldasia 2021, 43, 138–148. [Google Scholar] [CrossRef]
  120. Fisher, J. The Collared Turtle Dove in Europe. Br. Birds 1953, 56, 153–184. [Google Scholar]
  121. Gorski, W. Long-term dynamics of an urban population of Collared Dove (Streptopelia decaocto) from southern Baltic coast. Ring 1993, 15, 86–96. [Google Scholar]
  122. Hiley, J.R.; Bradbury, R.B.; Thomas, C.D. Introduced and natural colonists show contrasting patterns of protected area association in UK wetlands. Divers. Distrib. 2014, 20, 943–951. [Google Scholar] [CrossRef]
  123. García-Arroyo, M.; Gómez-Martínez, M.A.; MacGregor-Fors, I. The queen of the island: On the density and distribution of the Eurasian Collared-Dove (Streptopelia decaocto) in Cozumel. Urban Ecosyst. 2022, 26, 285–290. [Google Scholar] [CrossRef]
  124. Martínez-Ruiz, M.; De Labra-Hernández, M.A. Increased presence of the invasive exotic Eurasian Collared-Dove (Streptopelia decaocto) in the Chamela-Cuixmala Biosphere Reserve, Mexico, after hurricane disturbance. Huitzil 2022, 23, e629. [Google Scholar] [CrossRef]
  125. Robeck, P.; Essl, F.; van Kleunen, M.; Pyšek, P.; Pergl, J.; Weigelt, P.; Mesgaran, M.B. Invading plants remain undetected in a lag phase while they explore suitable climates. Nat. Ecol. Evol. 2024, 8, 477–488. [Google Scholar] [CrossRef] [PubMed]
  126. Mooney, H.A.; Cleland, E.E. The evolutionary impact of invasive species. Proc. Natl. Acad. Sci. USA 2001, 98, 5446–5451. [Google Scholar] [CrossRef]
  127. Hudson, R. The spread of the Collared Dove in Britain and Ireland. Br. Birds 1965, 58, 105–139. [Google Scholar]
  128. Coombs, C.F.B.; Isaacson, A.J.; Murton, R.K.; Thearle, R.J.P.; Westwood, N.J. Collared Doves (Streptopelia decaocto) in urban habitats. J. Appl. Ecol. 1981, 18, 41–62. [Google Scholar] [CrossRef]
  129. Bernis, F.; Asensio, B.; Benzal, J. Sobre la expansión y ecología de la tórtola turca (Streptopelia decaocto), con nuevos datos del interior de España. Ardeola 1985, 32, 279–294. [Google Scholar]
  130. Scheidt, S.N.; Hurlbert, A.H. Range expansion and populations dynamics of an invasive species: The Eurasian Collared-Dove (Streptopelia decaocto). PLoS ONE 2014, 9, e111510. [Google Scholar] [CrossRef] [PubMed]
  131. Parmesan, C.; Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 2003, 421, 37–42. [Google Scholar] [CrossRef]
  132. Fatemi, S.S.; Rahimi, M.; Tarkesh, M.; Ravanbakhsh, H. Predicting the impacts of climate change on the distribution of Juniperus excelsa M. Bieb. In the central and eastern Alborz Mountains, Iran. Iforest 2018, 11, 643–650. [Google Scholar] [CrossRef]
  133. Meng, J.; Li, M.; Guo, J.; Zhao, D.; Tao, J. Predicting suitable environments and potential occurrences for Cinnamomum camphora (Linn) Presl. Forest 2021, 12, 1126. [Google Scholar] [CrossRef]
  134. Xu, M.H.; Xue, X. Analysis on the effects of climate warming on growth and phenology of alpine plants. J. Arid Land Resour. Environ. 2013, 27, 137–141. [Google Scholar]
  135. Ma, B.; Sun, J. Predicting the distribution of Stipa purpurea across the Tibetan Plateau via the MaxEnt model. BMC Ecol. 2018, 18, 10. [Google Scholar] [CrossRef] [PubMed]
  136. Elton, C.S. The Ecology of Invasions by Animals and Plants; Chapman & Hall: London, UK, 1958. [Google Scholar]
  137. Nentwig, W. Biological invasions: Why it matters. In Biological Invasions; Nentwig, W., Ed.; Springer: Berlin/Heidelberg, Germany, 2007; pp. 1–6. [Google Scholar]
  138. Primack, R.; Sher, A.A. An Introduction to Conservation Biology; Oxford University Press: Oxford, UK, 2018. [Google Scholar]
  139. Global Invasive Species Database. Species Profile: Streptopelia decaocto. Available online: https://www.iucngisd.org/gisd/species.php?sc=1269 (accessed on 2 March 2020).
  140. National Commission for the Knowledge and Use of Biodiversity. Información Sobre Especies Invasoras en el Sistema Nacional de Información Sobre Biodiversidad. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, México. Available online: https://www.biodiversidad.gob.mx/especies/Invasoras (accessed on 23 June 2024).
  141. Nottingham, C.M.; Glen, A.S.; Stanley, M.C. Proactive development of invasive species damage functions prior to species reintroduction. Glob. Ecol. Conserv. 2019, 17, e00534. [Google Scholar] [CrossRef]
  142. Dukes, J.S.; Mooney, H.A. Disruption of ecosystem processes in western North America by invasive species. Rev. Chil. Hist. Nat. 2004, 77, 411–437. [Google Scholar] [CrossRef]
  143. Leger, E.A.; Espeland, E.K. Coevolution between native and invasive plant competitors; implications for invasive species management. Evol. Appl. 2010, 3, 169–178. [Google Scholar] [CrossRef]
  144. Spear, D.; Foxcroft, L.C.; Bezuidenhout, H.; McGeoch, M.A. Human population density explains alien species richness in protected areas. Biol. Conserv. 2013, 159, 137–147. [Google Scholar] [CrossRef]
  145. Klug, P.; Shiels, A.; Kluever, B.; Anderson, J.; Hess, S.; Ruell, E.; Bukoski, W.; Siers, S. A review of nonlethal and lethal control tools for managing the damage of invasive birds to human assets and economic activities. Manag. Biol. Invasions 2023, 14, 1–44. [Google Scholar] [CrossRef]
  146. Broennimann, O.; Treier, U.A.; Müller-Schärer, H.; Thuiller, W.; Peterson, A.T.; Guisan, A. Evidence of climatic niche shift during biological invasion. Ecol. Lett. 2007, 10, 701–709. [Google Scholar] [CrossRef]
  147. Pili, A.N.; Tingley, R.; Sy, E.Y.; Diesmos, M.L.L.; Diesmos, A.C. Niche shifts and environmental non-equilibrium undermine the usefulness of ecological niche models for invasion risk assessments. Sci. Rep. 2020, 10, 7972. [Google Scholar] [CrossRef]
  148. Elith, J.; Leathwick, J.R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 2009, 40, 677–697. [Google Scholar] [CrossRef]
  149. Jiménez-Valverde, A.; Peterson, A.T.; Soberón, J.; Overton, J.M.; Aragón, P.; Lobo, J.M. Use of niche models in invasive species risk assessments. Biol. Invasions 2011, 13, 2785–2797. [Google Scholar] [CrossRef]
  150. Barbosa, F.G.; Schneck, F.; Melo, A.S. Use of ecological niche models to predict the distribution of invasive species: A scientometric analysis. Braz. J. Biol. 2012, 72, 821–829. [Google Scholar] [CrossRef]
Sustainability 16 07071 g001
Figure 1. Geographical distribution of occurrence records for Myiopsitta monachus (A) and Streptopelia decaocto (B) across Mexico City Metropolitan Area and adjacent areas. First (▲) and subsequent occurrence records (●) were obtained from different databases and personal field surveys (see Materials and Methods Section 2.2).
Figure 1. Geographical distribution of occurrence records for Myiopsitta monachus (A) and Streptopelia decaocto (B) across Mexico City Metropolitan Area and adjacent areas. First (▲) and subsequent occurrence records (●) were obtained from different databases and personal field surveys (see Materials and Methods Section 2.2).
Sustainability 16 07071 g001
Sustainability 16 07071 g002
Figure 2. Potential distribution areas of Myiopsitta monachus [current (A) and future climate change scenarios in 2050: (B) SSP 126, (C) SSP 370, and (D) SSP 585 for IPSL-CM6A-LR, and (E) SSP 126, (F) SSP 370, and (G) SSP 585 for GFDL-ESM4] and Streptopelia decaocto [current (H) and future climate change scenarios in 2050: (I) SSP 126, (J) SSP 370, and (K) SSP 585 for IPSL-CM6A-LR, and (L) SSP 126, (M) SSP 370, and (N) SSP 585 for GFDL-ESM4] were estimated based on ensemble climate models.
Figure 2. Potential distribution areas of Myiopsitta monachus [current (A) and future climate change scenarios in 2050: (B) SSP 126, (C) SSP 370, and (D) SSP 585 for IPSL-CM6A-LR, and (E) SSP 126, (F) SSP 370, and (G) SSP 585 for GFDL-ESM4] and Streptopelia decaocto [current (H) and future climate change scenarios in 2050: (I) SSP 126, (J) SSP 370, and (K) SSP 585 for IPSL-CM6A-LR, and (L) SSP 126, (M) SSP 370, and (N) SSP 585 for GFDL-ESM4] were estimated based on ensemble climate models.
Sustainability 16 07071 g002
Sustainability 16 07071 g003
Figure 3. Analysis of multivariate environmental similarity surface (MESS) of Myiopsitta monachus [current (A) and future climate change scenario in 2050: (B) SSP 126, (C) SSP 370, and (D) SSP 585 for GFDL-ESM4 and IPSL-CM6A-LR, and (E) SSP 126, (F) SSP 370, and (G) SSP 585 for IPSL-CM6A-LR] and Streptopelia decaocto [current (H) and future climate change scenario in 2050: (I) SSP 126, (J) SSP 370, and (K) SSP 585 for IPSL-CM6A-LR, and (L) SSP 126, (M) SSP 370, and (N) SSP 585 for GFDL-ESM4] within the MCMA under climate change scenarios. Red areas show one or more environmental variables outside the range present in the training data, so predictions in those areas should be treated with strong caution, while blue areas are more similar.
Figure 3. Analysis of multivariate environmental similarity surface (MESS) of Myiopsitta monachus [current (A) and future climate change scenario in 2050: (B) SSP 126, (C) SSP 370, and (D) SSP 585 for GFDL-ESM4 and IPSL-CM6A-LR, and (E) SSP 126, (F) SSP 370, and (G) SSP 585 for IPSL-CM6A-LR] and Streptopelia decaocto [current (H) and future climate change scenario in 2050: (I) SSP 126, (J) SSP 370, and (K) SSP 585 for IPSL-CM6A-LR, and (L) SSP 126, (M) SSP 370, and (N) SSP 585 for GFDL-ESM4] within the MCMA under climate change scenarios. Red areas show one or more environmental variables outside the range present in the training data, so predictions in those areas should be treated with strong caution, while blue areas are more similar.
Sustainability 16 07071 g003
Sustainability 16 07071 g004
Figure 4. Suitable habitat areas of Myiopsitta monachus [current (A) and future climate change scenario in 2050: (B) SSP 126, (C) SSP 370, and (D) SSP 585 for IPSL-CM6A-LR, and (E) SSP 126, (F) SSP 370, and (G) SSP 585 for GFDL-ESM4] and Streptopelia decaocto [current (H) and future climate change scenario in 2050: (I) SSP 126, (J) SSP 370, and (K) SSP 585 for IPSL-CM6A-LR, and (L) SSP 126, (M) SSP 370, and (N) SSP 585 for GFDL-ESM4] in agricultural lands within the MCMA under climate change scenarios. Green areas show the suitable habitat of the invasive alien species (considering a threshold > 0.3); gray areas indicate the unsuitable habitat of the invasive alien species (considering a threshold < 0.3); and blue lines show the agricultural areas in the MCMA.
Figure 4. Suitable habitat areas of Myiopsitta monachus [current (A) and future climate change scenario in 2050: (B) SSP 126, (C) SSP 370, and (D) SSP 585 for IPSL-CM6A-LR, and (E) SSP 126, (F) SSP 370, and (G) SSP 585 for GFDL-ESM4] and Streptopelia decaocto [current (H) and future climate change scenario in 2050: (I) SSP 126, (J) SSP 370, and (K) SSP 585 for IPSL-CM6A-LR, and (L) SSP 126, (M) SSP 370, and (N) SSP 585 for GFDL-ESM4] in agricultural lands within the MCMA under climate change scenarios. Green areas show the suitable habitat of the invasive alien species (considering a threshold > 0.3); gray areas indicate the unsuitable habitat of the invasive alien species (considering a threshold < 0.3); and blue lines show the agricultural areas in the MCMA.
Sustainability 16 07071 g004
Sustainability 16 07071 g005
Figure 5. Suitable habitat areas of Myiopsitta monachus [current (A) and future climate change scenario in 2050: (B) SSP 126, (C) SSP 370, and (D) SSP 585 for IPSL-CM6A-LR, and (E) SSP 126, (F) SSP 370, and (G) SSP 585 for GFDL-ESM4] and Streptopelia decaocto [current (H) and future climate change scenario in 2050: (I) SSP 126, (J) SSP 370, and (K) SSP 585 for IPSL-CM6A-LR, and (L) SSP 126, (M) SSP 370, and (N) SSP 585 for GFDL-ESM4] in protected areas within the MCMA under climate change scenarios. Green areas show the suitable habitat of the invasive alien species (considering a threshold > 0.3); gray areas indicate the unsuitable habitat of the invasive alien species (considering a threshold < 0.3); and blue lines show the PAs in the MCMA.
Figure 5. Suitable habitat areas of Myiopsitta monachus [current (A) and future climate change scenario in 2050: (B) SSP 126, (C) SSP 370, and (D) SSP 585 for IPSL-CM6A-LR, and (E) SSP 126, (F) SSP 370, and (G) SSP 585 for GFDL-ESM4] and Streptopelia decaocto [current (H) and future climate change scenario in 2050: (I) SSP 126, (J) SSP 370, and (K) SSP 585 for IPSL-CM6A-LR, and (L) SSP 126, (M) SSP 370, and (N) SSP 585 for GFDL-ESM4] in protected areas within the MCMA under climate change scenarios. Green areas show the suitable habitat of the invasive alien species (considering a threshold > 0.3); gray areas indicate the unsuitable habitat of the invasive alien species (considering a threshold < 0.3); and blue lines show the PAs in the MCMA.
Sustainability 16 07071 g005
Sustainability 16 07071 g006
Figure 6. Piecewise linear regression of the annual distances from the M. monachus ((A); adjR2 = 0.033, p < 0.05) and S. decaocto ((B); adjR2 = 0.013, p < 0.05) first occurrence record in the MCMA provides rates of spread (V), corresponding to the slope of each segment, with black lines showing three possible phases of the spread process with two breakpoints (▲) in 2011 and 2016 for M. monachus and 2016 and 2019 for S. decaocto. Slopes ± SE values reveal two possible stages of expansion and potential saturation of these invasive species. Red line is the regression line, and green dots are the distances of the points of occurrence per year with respect to the first point of occurrence detected.
Figure 6. Piecewise linear regression of the annual distances from the M. monachus ((A); adjR2 = 0.033, p < 0.05) and S. decaocto ((B); adjR2 = 0.013, p < 0.05) first occurrence record in the MCMA provides rates of spread (V), corresponding to the slope of each segment, with black lines showing three possible phases of the spread process with two breakpoints (▲) in 2011 and 2016 for M. monachus and 2016 and 2019 for S. decaocto. Slopes ± SE values reveal two possible stages of expansion and potential saturation of these invasive species. Red line is the regression line, and green dots are the distances of the points of occurrence per year with respect to the first point of occurrence detected.
Sustainability 16 07071 g006
Table 1. Contribution of the bioclimatic variables to the MaxEnt modeling (%) for Myiopsitta monachus and Streptopelia decaocto.
Table 1. Contribution of the bioclimatic variables to the MaxEnt modeling (%) for Myiopsitta monachus and Streptopelia decaocto.
SpeciesVariableDescriptionPercent Contribution (%)Permutation Importance (%)
Myiopsitta monachusBio01Annual mean temperature0.43.2
Bio02Mean diurnal range4.04.0
Bio03Isothermality52.10.4
Bio05Max temperature of warmest month14.622.8
Bio06Min temperature of coldest month1.315.1
Bio11Mean temperature of coldest quarter17.444.0
Bio12Annual precipitation10.210.6
Streptopelia decaoctoBio02Mean diurnal range35.424.5
Bio03Isothermality8.65.2
Bio06Min temperature of coldest month 17.514.3
Bio10Mean temperature of warmest quarter10.58.1
Bio12Annual precipitation14.521.1
Bio14Precipitation of driest month5.910.8
Bio15Precipitation seasonality7.616.0
Table 2. Area (km2) variation in the suitable habitat of Myiopsitta monachus and Streptopelia decaocto under the current and future climate scenarios.
Table 2. Area (km2) variation in the suitable habitat of Myiopsitta monachus and Streptopelia decaocto under the current and future climate scenarios.
SpeciesIPSL-CM6A-LR GFDL-ESM4
CurrentSSP 126SSP 370SSP 585SSP 126SSP 370SSP 585
Myiopsitta monachus5642580958345486427351905273
(71%)(73%)(73%)(69%)(54%)(65%)(66%)
Streptopelia decaocto5489557654805518513452655506
(69%)(70%)(69%)(69%)(64%)(66%)(69%)
Table 3. Predicted changes in the climatically suitable habitat area (km2) of Myiopsitta monachus and Streptopelia decaocto under climate change scenarios.
Table 3. Predicted changes in the climatically suitable habitat area (km2) of Myiopsitta monachus and Streptopelia decaocto under climate change scenarios.
Species IPSL-CM6A-LRGFDL-ESM4
CurrentSSP 126SSP 370SSP 585SSP 126SSP 370SSP 585
Myiopsitta monachusStable5642
(100%)		5642
(100%)		5642
(100%)		5486
(97%)		4273
(76%)		5190
(92%)		5273
(93%)
Gain 167
(+3%)		192
(+3%)		----
Loss --156
(−3%)		1369
(−24%)		452
(−8%)		369
(−6%)
Streptopelia decaoctoStable5489
(100%)		5482
(100%)		5455
(99%)		5455
(99%)		5134
(93%)		5265
(96%)		4768
(88%)
Gain 94
(+2%)		15
(+0.3%)		63
(+1%)		--738
(+13%)
Loss 7
(−0.1%)		19
(−0.3%)		34
(−0.6%)		355
(−6%)		224
(−4%)		721
(−13%)
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ramírez-Albores, J.E.; Sánchez-González, L.A.; Prieto-Torres, D.A.; Navarro-Sigüenza, A.G. Where Are We Going Now? The Current and Future Distributions of the Monk Parakeet (Myiopsitta monachus) and Eurasian Collared Dove (Streptopelia decaocto) in a Megalopolis. Sustainability 2024, 16, 7071. https://doi.org/10.3390/su16167071

AMA Style

Ramírez-Albores JE, Sánchez-González LA, Prieto-Torres DA, Navarro-Sigüenza AG. Where Are We Going Now? The Current and Future Distributions of the Monk Parakeet (Myiopsitta monachus) and Eurasian Collared Dove (Streptopelia decaocto) in a Megalopolis. Sustainability. 2024; 16(16):7071. https://doi.org/10.3390/su16167071

Chicago/Turabian Style

Ramírez-Albores, Jorge E., Luis A. Sánchez-González, David A. Prieto-Torres, and Adolfo G. Navarro-Sigüenza. 2024. "Where Are We Going Now? The Current and Future Distributions of the Monk Parakeet (Myiopsitta monachus) and Eurasian Collared Dove (Streptopelia decaocto) in a Megalopolis" Sustainability 16, no. 16: 7071. https://doi.org/10.3390/su16167071

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop