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Long-term acoustic data collectionPassive acoustic monitoring was conducted from November 19, 2018 to October 3, 2020, with 683 days of recording effort overall (Supplementary Table 2), using a High-frequency Acoustic Recording Package (HARP)37. The HARP was deployed in Bahía Norte, Guadalupe Island, located approximately 150 miles offshore of México’s Baja California Peninsula (Fig. 1). The HARP was bottom-mounted and deployed to a depth of approximately 1100 m, with a calibrated hydrophone suspended ~30 m above the seafloor. The same hydrophone was used for both deployments to facilitate data comparison. The omnidirectional hydrophone sensor (ITC-1042, International Transducer Corporation, Santa Barbara, CA) had an approximately flat (±3 dB) hydrophone sensitivity from 10 Hz to 100 kHz of −200 dB re V/μPa. The sensor was connected to a custom-built preamplifier board and bandpass filter. The calibrated system response was corrected for during analysis. Data were sampled continuously at a 200 kHz sampling rate with 16-bit quantization, effectively monitoring a frequency range of 10 Hz–100 kHz.Automatic detection and manual classification of beaked whale echolocation clicksBeaked whales can be acoustically identified by their echolocation clicks38. These signals are frequency-modulated (FM) upswept pulses, which appear to be species-specific and are distinguishable by their spectral and temporal features. Cuvier’s beaked whale echolocation signals are well differentiated from the acoustic signals of other beaked whale species. They are polycyclic with a characteristic FM pulse upsweep, peak frequency around 40 kHz, and uniform inter-pulse interval of about 0.4–0.5 s39,40. Additionally, Cuvier’s beaked whale FM pulses have characteristic spectral peaks at approximately 17 and 23 kHz.Beaked whale FM pulses were detected in the HARP data with an automated method using the MATLAB-based (Mathworks, Natick, MA) custom software program Triton (https://github.com/MarineBioAcousticsRC/Triton) and other MATLAB custom routines. After all potential echolocation signals were identified with a Teager–Kaiser energy detector41,42, an expert system discriminated between delphinid clicks and beaked whale FM pulses. A decision about presence or absence of beaked whale signals was based on detections within a 75 s segment. Only segments with more than seven detections were used in further analysis. All echolocation signals with a peak and center frequency below 32 and 25 kHz, respectively, a duration less than 355 μs, and a sweep rate of More

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Correction to: Nature Ecology & Evolution https://doi.org/10.1038/s41559-019-0970-7, published online 2 September 2019.The Journal would like to note that the authors first made contact in September 2019 to raise the concerns that follow, and the Journal apologizes both for the delay in relaying these corrections publicly and for the changed instances that prevent making corrections to the original article itself. What follows is the Author correction.In the version of this article initially published, we made several errors in our R analysis code, and in the text and figures. First, the number of species with negative net dispersal velocity (net DV) were incorrectly calculated, resulting in slight changes in Fig. 2 and Supplementary Fig. 6, and in the text. The amended figures are provided below (Figs. 1–7). Changes to the text under the ‘DVs’ subsection of Results are: “When considering the mainstem pathway, we estimate that the mobile subpopulations of 134 (old version: 124) and 185 (old version: 174) (RCP 4.5 and 8.5, respectively) species will experience dispersal deficits in at least 50% of their southern Appalachian range, whereas these estimates increase to 229 (old version: 226) and 231 (old version: 232) species for the stationary subpopulation. Slow-climate-velocity tributaries reduce the number of species experiencing dispersal deficits by 99.3% (old version: 99.2%) and 90.3% (old version: 16.9%) (RCP 4.5 and 8.5, respectively) for the mobile component and 17.9% (old version: 90.8%) and 12.1% (old version: 12.9%) for the stationary component (Fig. 2a,b).” The two large discrepancies in dispersal deficit values (90.3% vs. 16.9%; 17.9% vs. 90.8%) were solely consequences of original text errors (16.9% and 90.8% values were erroneously switched), and not differences in calculations; therefore, the results did not change.Fig. 1Figure 2, original and corrected.Full size imageFig. 2Figure 3c,d, original and corrected.Full size imageFig. 3Figure 4, original and corrected.Full size imageFig. 4Figure 5, original and corrected.Full size imageFig. 5Supplementary Figure 6, original.Full size imageFig. 5Supplementary Figure 6, corrected.Full size imageFig. 6Supplementary Figure 8, original.Full size imageFig. 6Supplementary Figure 8, corrected.Full size imageFig. 7Supplementary Figure 9, original.Full size imageFig. 7Supplementary Figure 9, corrected.Full size imageSecond, we made errors when plotting Fig. 3c,d. Boxplots of mean change in habitat suitability were plotted instead of median change as specified in the caption; further, whiskers did not include the entire range of values. The amended figure is provided below. We would like to correct associated errors in text; specific changes are: “Our ENMs estimate a median 15.1% (old version: mean 14.1%) reduction (range −42.5% to +16.6% [old version: −51.6% to +2.4%] across 233 species) in habitat suitability associated with the tributary pathway compared with only a 3.8% (old version: 1.6%) reduction (−11.0% to +12.3% [old version: −7.9% to +1.8%]) for the mainstem pathway due to differing non-temperature habitat conditions (Fig. 3a,b).”Third, we made errors when plotting Fig. 4 and Supplementary Fig. 8. Specifically, net DV values were incorrectly rescaled; one extinct species was erroneously included in the plot; and the number of species in each quadrant was counted incorrectly. The amended figures are shown below. We would like to add a sentence (“The y-axes are inverse hyperbolic sine (asinh)-transformed”) to the caption of Fig. 4 to describe the y-axis scaling in the amended figures. There were two other text errors in the caption. The phrase “Mean net DV” should have been “Median net DV,” whereas the phrase “mean habitat suitability” should have read “median change in habitat suitability.” Therefore, the corrected Fig. 4 caption should read: “Species-level mismatch between net DV and upstream habitat suitability. a–d, Median net DV of mobile (a,b) and stationary subpopulations under the RCP 8.5 scenario plotted as a function of median change in habitat suitability for mainstem (a,c) and tributary (b,d) dispersal pathways. Each point represents a species and is computed as the median response across all projected occupied reaches. Red and blue numbers correspond to the number of species in each of the four quadrants. The y-axes are inverse hyperbolic sine (asinh)-transformed. e–h, Four species highlighting the diversity in dispersal-based and habitat suitability-based vulnerability: streamline chub (e); brook trout (f); flathead catfish (g); blacknose dace (h). Credit: David Neely (e–h)”. These corrections did not change our inferences.Fourth, there were errors in rescaling and plotting net DV values and in the calculation of quadrant percentages in Fig. 5 and Supplementary Fig. 9. The amended figures are shown below. We would like to add two sentences at the end of Fig. 5 caption to provide greater detail on plotting methods: “The y-axes of the scatterplots are inverse hyperbolic sine (asinh)-transformed. For clarity, the scatterplots show net DV values ≥ −13,000 and ≤ 130, and change in habitat suitability values ≤ 100, representing >99.5% of all observations.” There was one other text error in the caption: the phrase “mean habitat suitability” should have read “mean change in habitat suitability.” Therefore, the corrected Fig. 4 caption should read: “Community-level mismatch between net DV and upstream habitat suitability. a–d, Mean net DV of mobile (a,b) and stationary (c,d) subpopulations under the RCP 8.5 scenario plotted as a function of mean change in habitat suitability for mainstem (a,c) and tributary (b,d) dispersal pathways. Each point (scatterplot) and reach (map) is computed as the mean response for all species projected to occur within the reach. Quadrant numbers represent percentage of reaches in the quadrant. Colours associated with the upper-right quadrant correspond to ‘safe’ reaches where community members can keep pace with ISVs and habitat suitability increases. Colours associated with the lower-left quadrant correspond to ‘vulnerable’ reaches where community members cannot keep pace with ISVs and habitat suitability declines. The y-axes of the scatterplots are inverse hyperbolic sine (asinh)-transformed. For clarity, the scatterplots show net DV values ≥ −13,000 and ≤ 130, and change in habitat suitability values ≤ 100, representing >99.5% of all observations.” These corrections did not change our inferences.Fifth, there was an error in the last sentence of the “Calculating net DVs” subsection in Methods: “Last, we calculated the mean net DV for each species (species-specific DV) by averaging net DVs at all occupied reaches, as well as the community-wide net DV at each stream reach (reach-specific DV) by averaging the net DVs of all species at each reach.” This sentence should have read “Last, we calculated the median net DV for each species (species-specific DV) across all occupied reaches, as well as the mean community-wide net DV at each stream reach (reach-specific DV) by averaging the net DVs of all species at each reach.”Corrections of calculation errors yielded results that were similar to those in the original analysis whereas corrections of plotting and text errors did not affect our original inferences. Therefore, these errors did not change the overall results and conclusions of the article. We sincerely apologize for any misunderstanding and inconvenience caused by these errors. More

Study areaThe study area was located in Northeastern Italy (Fig. 2). Specifically, it encompassed 13 municipalities in the Valbelluna (located in Belluno Province), Valsugana, and Cembra valleys (located in Trento Province). The study area has a sub-continental, temperate climate, with cold, often snowy winters and warm, mild summers. Human settlements consist mainly of small villages composed of country houses with private gardens and public parks, all surrounded by forested areas; among the sampled municipalities, only Belluno and Feltre had more than 10,000 inhabitants.Figure 2Study area. Points represent the sampling sites marked with the ID number as in Tables 2 and 3. Background satellite image from Sentinel-2 cloudless (https://s2maps.eu), and urban places from OpenStreetMap contributors (https://openstreetmap.org). Map created using QGIS 3.22.Full size imageHost surveyThe presence and abundance of domestic animal hosts in each site were estimated through a door-to-door census. As the flight range of Ae. koreicus is unknown, a field inspection was performed within a 200-m radius of the sampling site, corresponding to the average flight distance of Ae. albopictus recorded in a study conducted in Italy46. The survey was carried out once in 2020. Residents were asked if they owned animals (dogs, cats, farm animals) and how many they had or, where possible, they were counted directly by the study team (visual inspection). The presence of wild ungulates was estimated according to data provided by the Forestry and Fauna Service—Wildlife Office of the Autonomous Province of Trento. The wild ungulate census was carried out in spring by visual inspection along transects, and repeated three times by hunters and personnel of the wildlife management provincial office.47. The average number of roe deer, red deer, and chamois in 2020 was considered for the analyses. Collected information was used to qualitatively estimate potential host availability in the sampling areas. Human population density in the areas surrounding the sampling point was estimated using the Global Human Settlement Database (GHS Data)48.Collection of Aedes koreicus and blood meal analysisSampling was carried out from 2013 to 2020 (from May to October) with different frequencies in the various years; most collections were made in 2020 (20 collections) and just one in 2019. In total, 23 different sites were sampled where Ae. koreicus were known to be present: 14 in Trento and 9 in Belluno Province, respectively (Table 1 and Supplementary Table S1 online), with altitudes ranging from 234 to 775 m a.s.l.6,16. Engorged mosquitoes were collected in public and private houses, garden centers, cemeteries, and from periurban dry-stone walls using a home-built handheld aspirator (a modified handheld vacuum) (Fig. 3). Mosquitoes were aspirated from shady areas under vegetation, walls, and catch basins. In addition, all engorged females collected during routine invasive mosquito surveillance were used for the analyses. In this surveillance, BG-sentinel traps (Biogents AG, Regensburg, Germany) baited with a BG-Lure cartridge (Biogents) were activated for 24 h fortnightly. Immediately after collection, each sample was placed in a cooler, transported to the laboratory, and stored at − 80 °C until molecular analysis.Figure 3Home-built handheld aspirator (a modified handheld vacuum).Full size imageSampled mosquitoes were identified at species level according to Montarsi et al.21 and ECDC guidelines for invasive mosquito surveillance in Europe49. Blood-fed females were isolated from collected mosquitoes to identify the blood meal host.DNA of single blood-fed mosquito samples, collected from 2013 to 2016, was extracted using Microlab Starlet automated liquid-handling workstations (Hamilton), using a MagMAX Pathogen RNA/DNA kit (Applied Biosystems, USA), according to the manufacturer’s instructions. DNA of a single abdomen of blood-fed mosquitoes, collected from 2017 to 2020, was extracted using QIAamp DNA Investigator kit tissues (Qiagen, Germany), following the manufacturer’s protocol. All samples were analyzed using a nested PCR with a specific set of primers targeting the vertebrate mitochondrial cytochrome c oxidase subunit I (COI) gene, as previously described50. The first PCR reaction was carried out in a total volume of 50 μl, containing 2 units of AmpliTaq Gold DNA Polymerase (Applied Biosystem, USA), 5 μl of 10X Buffer, 2.5 mM of MgCl2, 0.2 mM of each dNTP, 2.5 μl of DMSO, 0.2 mM of primers M13BCV-FW (5’-TGT AAA ACG ACG GCC AGT HAA YCA YAA RGA YAT YGG-3’) and BCV-RV1 (5’-GCY CAN ACY ATN CCY ATR TA-3’), and 5 μl of extracted DNA. The second PCR reaction was carried out in a total volume of 50 μl containing 2 units of AmpliTaq Gold DNA Polymerase (Applied Biosystem, USA), 5 μl of 10X Buffer, 2.0 mM of MgCl2, 0.2 mM of each dNTP, 2.5 μl of DMSO, 0.4.mM of primers M13 (5’-GTA AAA CGA CGG CCA GTG-3’) and BCV-RV2 (5’-ACY ATN CCY ATR TAN CCR AAN GG-3’), and 1 μl of the PCR products obtained during the first amplification step. The thermal profile of the first PCR consisted of activation at 95 °C for 10 min, followed by 40 cycles at 94 °C for 40 s, 45 °C for 40 s, and 72 °C for 1 min, with a final extension step of 7 min at 72 °C. The thermal profile of the second PCR consisted of activation for 10 min at 95 °C followed by 16 cycles of a touchdown protocol at 94 °C for 40 s, decreasing the annealing temperature from 60 °C to 45 °C for 40 s (1 °C/cycle), followed by 72 °C for 1 min. Then, 30 cycles at 94 °C for 40 s, 45 °C for 40 s, and 72 °C for 1 min, with a final extension step of 7 min at 72 °C. Negative controls were included during the extraction and amplification stages to confirm avoidance of contamination.The amplicons were sequenced in both directions using a 16-capillary ABI PRISM 3130xl Genetic Analyzer (Applied Biosystems, USA). To identify the blood meal host species, nucleotide sequences were compared with representative sequences available in the GenBank database using the Basic Local Alignment Search Tool (BLAST). Positive identification was made when  > 97% identity was attained between the query and subject sequence.Statistical analysisAs most of the identified hosts were either humans or wild ungulates (see Results), we investigated how the probability of feeding on these two host groups was affected by different abiotic factors. Specifically, we considered two binary response variables indicating whether or not the blood meal was acquired from a human/wild ungulate host. We developed univariate (i.e., with only one explanatory variable) generalized linear models (GLMs) with a binomial-distributed error structure, considering in turn, for each response variable, the following four explanatory covariates: (i) the altitude of the sampling point; (ii) the human population density in the area surrounding the sampling point, defined as 250 m square units, as per the Global Human Settlement Database48; (iii) the percentage of non-artificial land cover within different buffers (100, 250 and 500 m radius from the sampling point), as per the Corine Land Cover dataset (defined as the sum of the fractions of agricultural and forested areas)51; the distance associated with the model with the lowest AIC value was then selected; (iv) the minimum distance of the sampling point from the nearest pixel labeled as forest, according to the Corine category. All analyses, including plot creation, was performed using R v4.0.252 and “tidyverse”, “ggplot2”, and “gridExtra” libraries.Map in Fig. 1 was generated by QGIS 3.22 using Sentinel-2 cloudless as background satellite image and urban places from OpenStreetMap database53,54,55. More

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