Posted on Authorea 18 May 2020 — CC-BY 4.0 — https://doi.org/10.22541/au.158981471.19903295 — This a preprint and has not been peer reviewed. Data may be preliminary. pce lovr ntersniiiyt hfigevrnetlcniin,adwl epn ieetyto differently respond will and conditions, environmental conditions. shifting environmental to changing sensitivity upon their dependent in and vary dynamic also course, interactions Species of biotic by are, & set distributions (Elith constraints Geographic variables are incorporating that by 2016). predictor requirements their Reid, 1957) species of (or & (Hutchinson, into variables Montgomery, insights set niche” environmental (Leach, new “fundamental of large provided the have use empirical a SDM the to habitat, to serious with models akin suitable limited distribution data a of generally predictors species is presence posing as employ approach proxies) species numerous, the to Although biotic explain is be and 2009). approach to to Leathwick, abiotic One attempt The expected quantification. which limited. are and are (SDM), distribution identification distributions their this geographic to their determine challenge that jointly is that species all factors of attribute universal A INTRODUCTION across variable highly is that change climate of important effects potentially time. has with over change and consistent differences climate regions are hybridize, These geographic can results species, predictions. and our similar future Overall ecologically all are ramifications. for in species eco-evolutionary requirements ranges these overlapping Because environmental in different ranges. distinct increase for their an evidence is provide There optima edges. in range around fluctuations slight esnlt,wiepeiiaino h retqatradma eprtr ftecletqatrwr togpeitr of predictors strong were quarter coldest the of to temperature predictor mean strongest and CO the the quarter different that calculated driest under pennsylvanica found and the variables We P. distributions, environmental of species. future same precipitation two the for while the seasonality, using maps between distributions suitability overlap future binary of predicted created degree project then (RCP), to We Pathways and Concentration the ArcMap. habitat Representative maintaining on variables suitable species environmental current identified both predict we for WorldClim, to from MAXENT data of use environmental species distribution and we correlates databases which Here, of museum models quantify. from distribution implications related variables. to data species closely the predictor employ difficult two understanding to of of be is and distributions set may approach future taxa, that a One many factors with challenge. in numerous a data and by reported presence priority limited been a are have remains distributions shifts shifts these their range that change-induced is climate species Furthermore, of attribute universal A Abstract 2020 18, May 2 1 Zhang Mengyuan of Vicki shifts ( range bug the ambush on hybridising effects two predicted different has change Climate nvriyo Toronto of University Mississauga - Toronto of University 2 ocnrtos utberne for ranges Suitable concentrations. hmt americana Phymata ags uuerne for ranges Future ranges. 1 ai Punzalan David , and hmt pennsylvanica Phymata Phymata .pennsylvanica P. .americana P. pce nlgto nhooei lmt hne sn pce occurrence species Using change. climate anthropogenic of light in species Phymata 2 n ok Rowe Locke and , r rdce oices otwswr n otwr thigher at southward and northwestward increase to predicted are r ntal rdce oices,bteetal eraewith decrease eventually but increase, to predicted initially are 1 n rae iaysiaiiymp fcretdistributions current of maps suitability binary created and , species ) .americana P. 2 .americana P. and .pennsylvanica P. agswsprecipitation was ranges conigfor accounting , Posted on Authorea 18 May 2020 — CC-BY 4.0 — https://doi.org/10.22541/au.158981471.19903295 — This a preprint and has not been peer reviewed. 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Google using manually data these supplemented we data, longitude and before latitude verification lacking accessions identification For secondary requires the BugGuide.net on from publishing on (https://www.inaturalist.org/projects/uncovering- collected authority bugs” species ambush an Data the the Riverside), the-ambush-bugs). “Uncovering if (UC project included iNaturalist Masonick was the Paul iNaturalist.org of science by from citizen verified two collected was from Data identity Inventory at data BugGuide.net. gathered Bug Collection, and supplemented also Plant Entomological iNaturalist.org We were Identifications (http://research.amnh.org/pbi/). websites, the Spencer data AMNH Zoology. and the the These (https://www.zoology.ubc.ca/entomology/) of by by analyses. maintained Museum provided Museum Biodiversity subsequent databases Michigan Beaty museum from of the from excluded University available were the information DP the Uni- with and Museum, by at Collection, National (DP) questionable Col- Smithsonian Insect authors National Museum, considered the Canadian Ontario Guelph Royal Pittsburgh, of of Nematodes, of one and versity Museums by Arachnids, Carnegie examined Insects, (AMNH), of specimens History lection from Natural of compiled Museum was American data distribution bug Ambush two METHODS the AND of MATERIALS ranges that respective hypothesized we the warming, increase. limiting climate would We anthropogenic are ranges these change. given overlapping factors of climate distributions; future abiotic We current range anthropogenic their their of historical candidate ranges. in recent scenarios of future resulting several and there sets species, and current under Thus, different current distributions the ecology. that their characterize future their hypothesized to forecast predicting regarding and SDM and knowledge and species, requirements of two data habitat shortage climate a their biogeographic clima- is understanding used of there in importance histories, value the life supports is & indirectly their Rodd, of 2015) to (Punzalan, ecology Rowe, consequential activity the & between-species in mating (Punzalan and variables to traits within- environmental tic linked and these (e.g., traits, is 2005), in variables melanic Yong, variation which climatic to 1941; linked latitudinal thermoregulation, species, 1939, been the in have (Balduf, abilities of role sites 2008a).Thermoregulatory hunting one Rowe, key as least a species at play plant In temperature) of overlap. temperate (Masonick, range niche in two wide occurring considerable mor- predators a the suggesting substantial generalist utilize 2013); are between despite they Swanson, species 2020), distinguish where Both Weirauch, 2017; 2017). habitats, to & Rowe, Rowe, & Masonick fail & (Punzalan 2017; divergence data (Punzalan Weirauch, phological Hy- phylogenetic inferred & States. Rabitsch, or molecular United Frankenberg, northeastern suspected current Michael, American the been this, the in has with of concentrated across populations consistent mostly west pair latter wild extending parapatric the distribution, in a and in bridisation prairies, in northerly Canadian shifts more and former range Midwest ranges. the respective potential with their evaluate Reduviidae), of to (Family: regions overlapping opportunity in and hybridize the on to Melin responses “window” took appear the that important we species reasons, particularly paper, insect these a present For as the 2010). touted 2015). In Knaap, been Harrison, in & has der and, Larson, change van 2016) (Taylor, environmental change & Hiscock, species (Vallejo-Mar´ın to climate & Mkumbo, alien hybridisation species between (Njiru, of in hybridizing contact expansion frequent collapse result of more and species can to invasion led this cases, the there also has and to some and conditions linked species, climatic timescales, Changing ecological being separate geological climate 2012). its historically over Early, in & composition predicting While changes Qian, community Sax, taxa, rapid variation (Guo, in 2009). Garaci, of many climatic changes al., evidence example, & in natural For growing et shifts 2006) Neilson, is to (Loarie (Parmesan, linked range Markham, challenge altogether undoubtedly central change-induced conditions Malcolm, a is climate new remains 2006; of implications track evolutionary Thomas, to evidence and & failure growing Fox, of is possibility there Hill, the Roy, including (Hickling, 2002), changes same the .pennsylvanica P. Phymata epg n a sue ob accurate. be to assumed was and webpage adishaetoo h otcmo ot mrcnseisi h genus the in species American North common most the of two are Handlirsch Phymata lhuhteei vdneta hi clgclrqieet are requirements ecological their that evidence is there Although . 2 Phymata ytmtc n curator and systematics hmt americana Phymata Posted on Authorea 18 May 2020 — CC-BY 4.0 — https://doi.org/10.22541/au.158981471.19903295 — This a preprint and has not been peer reviewed. Data may be preliminary. eebsdo h vrgdvle o 0rn.Apeiiayrnwscnutduigal1 bioclimatic 19 all using conducted runs, was analyses all run subsequent in preliminary Our model A grid. logistic runs. a a 10 within assumed presence for and bug values phase, ambush about averaged ‘training’ of information the the probability is on the for there based of data which were estimate the for an of grid gives 25% a which variables. aside within environmental found set the within was randomly using absence occurrence We species model the species’ a the In a When improves limited. of default). MAXENT is probability conditions, (the data the environmental often 0.5 when assumed approach was even we an grid robust conditions, a (MAXENT), considered environmental and distributions about data geographic information presence of species’ to of restricted when modeling favoured entropy maximum these from used We distributions the RCPs; business-as- four the the RCP RCP8.5, of MAXENT As each to 2.6. to up of scaled well, mapped. RCP was lowest are as variable the models increases bioclimatic by concentration Each represented gas scenario. is usual trajectories greenhouse gases (RCP) atmospheric concentration. greenhouse Pathways gas the variables the atmospheric greenhouse Concentration increases, Climate from for atmospheric Representative on scenario (IPCC). (GCM) four dependent best-case Change scenarios model with The change Climate data 2070, climate climate on possible from and global four Panel downscaled 2050 representing (CCSM4) Intergovernmental were into currently data the Model is projected Future by data System there were variables. distributions, Report (as Climate bioclimatic future Assessment version 11 Community map Fifth (1.4) same (see the To original the runs using species. Worldclim’s using MAXENT each from version), created numerous for from 2.0 collected on used were derived updated based variables variables no 1) collinearity, bioclimatic climate Appendix to 11 future S1.2, due in from r (Table Pearson’s problems resulted variables with minimize This variables bioclimatic below). of to pair 19 removed to any data (approximately was included For used environmental measurements. arcminutes variable and This was which precipitation 2.5 distributions. 2000 data and 2017), in bug environmental temperature to Hijmans, monthly ambush current was 1970 on Worldclim’s & data variables from of (Fick grid environmental version ranged of The database (2.0) influence WorldClim Antarctica. updated the for the The investigate except from kilometers). areas square form land 4.5 prevent raster global to in 1970-2000. order all from in data covered data done climate environmental was contained This obtained in which distribution. 1 We data current Appendix bioclimatic the S1.1, Table as the (see this with and 1970-2000 distributions to mismatch from refer species, temporal current data we both the and and restricted of Information), historical we of ranges Supporting analyses, the overlap the subsequent For of the in 1). changes in comparisons Appendix opposing increase visual are Additionally, an there study. suggested that current suspicion the ( our motivated shift with northward consistent a was indicating This increasing, were latitudes of “histori- (Mann-Whitney median latitudes and median “current” shift 1970, that the southward before suggests of sightings a species inspection to each Preliminary referring of 1970-present. as “historical”, distributions from well to: cal” sightings as according collector/photographer). to longitudes, subsets, the referring or two “current” by latitudes into of and of divided uncertain outside entry also as were data was indicated that the data points (i.e. in Locality data unreliable errors any of were removed result that we a identifications were and species that inspected, any America mitigate were To North datasets reporting. of the and range methods errors, the sampling such possibility in of the heterogeneity hours identifications, effects is species and as the confirm 1. inevitable, degrees to to is Figure lengths purposes, down individuals to in misidentifying present go accurate of mapped databases the were science are For citizen cases, sightings and building. some museum all Although or in of is Earth, station, and, localities This Google level, research used). 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Data may be preliminary. motn atra niae ntersos uvs(iue2,tog nti aei a rcptto of Precipitation was variable it the case was this in range, though temperature 2), annual (Figure curves the response to the range in indicated temperature For as diurnal importance. factor Isothermality, permutation the important 2). greatest (Figure of precipitation the ratio monthly with in a of variation probability annual as the low highest with to the defined contribution localities the from percent or that precipitation seasonality, relative indicate precipitation monthly largest curves the (see of Response occurrence with indicating deviation variable of ranges. 0.90, the environmental predictor than strongest the (BIO15), the was greater Seasonality as average, were identified Precipitation data were 2). temperature test and Appendix and precipitation (ROC), training species, Curves 2005). both both Operating Erhard, For for Receiver sensitivity) & (AUC) Thuiller, (i.e., the (Ara´ujo, Pearson, Curve positives of performance true the Inspection model of Under excellent maximized. ratio for Area was the 104 the 1–specificity) available, as and (i.e., was supported, data well positives statistically climate false were which to approach for interval MAXENT the the by within available were americana observations P. 226 of total A species two the RESULTS between overlap future of in degree change percent the the and ratios, these trajectories quantified. Using RCP potential habitat. was different indicating unsuitable species, under and suitable both distributions for for predicted grids habitat of suitable number = for total “1” the habitat species; “0” suitable both and = for “1” “2” habitat overlap; remained unsuitable habitat = unsuitable “0” current only; and habitat suitable for current the respectively while habitat, suitable of for of habitat ranges suitable predicted The habitats. The of zones. ranges contact between predicted = potential overlap “2” in of suitable”; changes percent remains assess The habitat the “0”. suitable calculated = assigned We “1” was become unsuitable; will habitat suitable. habitat remains becomes unsuitable suitable habitat habitat = future unsuitable “-1” unsuitable and in: = resulted “2” “0” distributions assigned predicted unsuitable; future was and current habitat between suitable difference future both For while distribution. the index” “0”, predicted habitat, change current suitable in the status change from “suitability the and subtracted compare the was to calculate order distribution In to predicted 2015). used future Rabinovich, & Jones, methods (Ceccarelli & used from from (Rebelo adopted commonly drawn (SSCI), observers were more different calculations is by math time and Raster of threshold period conservative predicted longer more the a of a over 10% collected in 2010). while binary data results range, bug distribution create potential This species ambush the to erroneous. with predict of used accurately be probability will threshold may binary occurrences predicted occurrences The to predicted the the converted 10.6.1). of visualize then (ESRI 90% to were that ArcMap used maps “10 on projections were Heat was generated thresholds Geographical conditions. maps maps were average. using environmental heat their curves maps future of at and Response presence/absence form set current variables. were the under these predictors in occurrence other of models all importance the while environmental relative of continuous predictor different individual the of each effect determine the for removed. evaluate to were to gain with plots and correlated 0% jackknife variables, were and with variables curves variables these response the If inspected model, We model. the the to to contributed gain that 0% variables contributed other that variables identify to variables .pennsylvanica P. th n 2 for 122 and .americana P. ecnietann rsnelgsi hehl” hstrsodwsslce si assumes it as selected was threshold This threshold”. logistic presence training percentile .pennsylvanica P. urn utbehbttwsasge 1 n urn nutbehbttwsassigned was habitat unsuitable current and “1” assigned was habitat suitable current , .pennsylvanica P. utatn atr eutdi:“1 utbehbttfor habitat suitable = “-1” in: resulted rasters Subtracting . .pennsylvanica P. .pennsylvanica P. n h aemto a sdt aclt h hnei suitable in change the calculate to used was method same the and TbeS.,Apni ) sn hs aa h oesproduced models the data, these Using 1). Appendix S1.1, (Table .americana P. .pennsylvanica P. a elsie ob 0 o nutbehbtt n “2” and habitat, unsuitable for “0” be to reclassified was ny h trbtstbefrec eeae a gives map generated each for table attributes The only. 4 and rcptto a loipiae san as implicated also was precipitation , .americana P. .pennsylvanica, P. .americana P. curnewsa lower a at was occurrence a utatdfo the from subtracted was rjce o 2070 for projected .americana P. .americana P. .americana P. , to Posted on Authorea 18 May 2020 — CC-BY 4.0 — https://doi.org/10.22541/au.158981471.19903295 — This a preprint and has not been peer reviewed. Data may be preliminary. ugssta h agso ohseismyb bet epu ihsottr rdce lmt hne But change. climate predicted short-term with up keep for of to expansion expansion able range range be the may larger both 2070, species a by of both of with expansions ranges range but the predict that 2050, forecasts suggests into Our forecasted requirements. niche is respective species their to correspond may which of responses different predict models Our world changing a in Distributions habitat of an increase now 0.1% is a DISCUSSION unsuitable is previously there was RCP6.0, that at Conversely, habitat inhabit. species. of can decrease amount both species greatest small the for both the unsuitable a or in with one that result decreases, either indicates will habitat where This unsuitable habitat area of RCP8.5 suitable amount at of the 0.4% amount 8.5, of the and contain 4.5 in only RCP2.6, changes currently at contraction the that Additionally, a that habitats of suggests to RCP6.0, shift This an translates range at increase. This However, to For (Figure RCP8.5. RCP6.0. predicted species species. and at two is single RCP6.0 occurs the a 2.8% at of of only occurs ranges area contain which overlapping that in 0.7%, regions increase is slight of overlap a in is increase and habitats, there unsuitable largest suitable trajectories, become of RCP to all percentage expected relative At is the RCP8.5 habitat in and suitable suitable change RCP6.0 of where predicted at edges percentage versa. range constant no the vice around remain is that fluctuations or there indicate some habitats RCP4.5 are Although 2070 suitable and there for habitats. of RCP2.6 predictions suitable percentage an However, current the at 2050. to in decrease in change will predicted no either 4.5 is habitat of there RCP Notably, an southward. at is expansion range for to predicted habitats northwestward Similar mostly suitable 2070. is for and of increase increase 2050 2.0% habitats percentage range for a the the and projected of 2050 in when direction in extent increase The increase The 2.7% habitats. a southward. suitable with current and RCP6.0, to at for compared increase occurs 2070, to habitats in predicted suitable is of habitats increase suitable percent of test percentage the for Generally, models 1). (Table suitability habitat as mapped vanica were distributions future Predicted temperatures. the optimal was at it occurrences of as -6 probability (BIO11) of of -2 Quarter probability about temperature Coldest of The precipitation. a the temperature optimal importance. of a at permutation at Temperature occurrences greatest greatest Mean of the probability the the with for of of variable support probability half the was than millimeters, during there less 300 millimeters Additionally, to of 200 levels drastically precipitation about decreases above of occurrence and precipitation millimeters optimal 140 of an which levels suggest in quarter, models driest The the (BIO17). Quarter Driest the iru ihteevrnet n htseisrne r xetdt hf sacneuneo changing of consequence despite a example, For as 2005). shift Thuiller, equi- to & in expected Guisan currently are 2010; are Phillips, ranges species & species that Kearney, that (Elith, is and conditions models environmental environment, the the of with assumptions librium underlying underappreciated environmental validating (presumably) Naturally, sometimes movement and range. the A “current” occurrence as the updating the as and that to observation require referred noted we will be which models proceeds. 2000, the should from to It derived 1970 predictions contract. spans the study or this same in used the data remain to predicted were tRP. Fgr ) n h rdce hne nsial aiasaesmaie spercentages as summarized are habitats suitable in changes predicted the and 3), (Figure RCP8.5 at .pennsylvanica P. .americana P. ° ,tepoaiiyof probability the C, .pennsylvanica P. oad h ag of range the towards sa08 nraewihocr tRP. n25,adtedrcino the of direction the and 2050, in RCP6.0 at occurs which increase 0.8% a is .americana P. ° uigtecletqatr bv eprtr f3 of temperature a Above quarter. coldest the during C .americana P. .pennsylvanica P. speitdt emr oet while modest, more be to predicted is a h ihs rbblt focrec.Blwprecipitation Below occurrence. of probability highest the has .pennsylvanica P. .pennsylvanica P. .americana P. 5 and .pennsylvanica P. curne eraet esta afo the of half than less to decrease occurrences h raetpretices fsuitable of increase percent greatest the , h ags euto npeitdrange predicted in reduction largest the eutn nalre ereo overlap. of degree larger a in resulting , .americana P. .pennsylvanica P. .pennsylvanica P. oatrpgncciaechange, climate anthropogenic to .americana P. .americana P. .pennsylvanica P. seApni ) This 3). Appendix (see cusa lesser a at occurs .pennsylvanica P. curne was occurrences and .americana P. ° n below and C .pennsyl- P. h grea- The . 4 ranges .The ). Posted on Authorea 18 May 2020 — CC-BY 4.0 — https://doi.org/10.22541/au.158981471.19903295 — This a preprint and has not been peer reviewed. Data may be preliminary. dniytevralsta r addtsfrdtriigterne of ranges of the distributions determining for overlapping niche candidates yet are imperfect distinct that but The variables similarity, the 2017). ecological identify Althoff, high & exhibit (Darwell may americana coexistence range permit geographic will same overlap the inhabit upon that contingent SDM Species are of distributions niche use these a The but of contractions. determinants shifts Abiotic or range modelling; predicting expansions or modelled for used. range sampling scenarios tools predictions, short-term additional emission critical these by by the are supplemented in obscured MAXENT ultimately present be as are variability range may such that the wider trends guides a to long-term as encompass Due any used future that emissions. For potential be pathways gas 2008). several should concentration capture be Monahan, greenhouse scenarios projected to also & future order other may (Parra possible in several sets shifts projections of are data range change there climate climate of but four in trends used distributions, predicted incorporated study Additionally, uncertainty present distributions 2010). our of current instance, though amount al., even century, the et the were on (Rebelo of species end dependent expand where projections the reported, at RCP to change been higher predicted climate also and to has were variation due have expansions Temporal extinction may range 2018). face RCPs) al., induces to et predicted different RCP (Wang lower (i.e., contractions both range a Jones, scenarios investigated to & where emissions Tarroso, have lead distributions, Different (Rebelo, that on species 2017). studies of effects Biondi, in species, groups opposite & and seen across 2017) D’Alessandro, is Feng, variable Urbani, & future have Wei, shifts 2010; the highly Ning, range in 2015; of is (Dowling, timepoints spectrum species different that diverse individual at a change taxa, scenarios Ohlem other change climate Hill, In climate of (Chen, 2002). al., documented effects et well (Menzel with been time over consistent and regions are geographic current results our the ramifications. Nevertheless, that eco-evolutionary our recognition extirpation. important growing Overall, local have a to of can regimes, adds warming rate environmental and climate and There hypotheses, in of generating possibility dynamics). shifts for trajectory the spatial point metapopulation underestimate starting track and/or may a successfully provide adaptation models range always models the predicted local will the case populations to which beyond do that due or in but guarantee at (e.g. habitats, no persist habitat also suitable successfully & ‘preferred’ is potentially may Elith the populations of 2017; some of (Anderson, projections that margins distributions make possibility species only the (Bulgarella, with modeling exclude models interactions interactions in not the biological biotic frontiers Furthermore, of omit the 2009). integration typically of Leathwick, the they one and that models, remains 2012). 2014), in our Nilsson, information Morgan-Richards, & SDM in abiotic Jansson, & of for factors (Hof, Jacobson, limitation accounted of distributions Minards, critical not realized Trewick, number a are on & highlights interactions a influence (Goldberg also biotic important on displacement have Such This character depend certainly 2016). of almost possibility Pierce, and will the & predict, but and Kelly, arena hybrids, range to Pfennig, larger of predicted 2007; difficult fitness a the the Lande, and are realized, dispersal, opportunities consequences of are hybridisation rates conditions potential it including increased climatic Although The as suggest in model. competition. change overlap) changes the not for range by forecasted do predicted potential the conditions as (and whether winter effects, expansions seen future lesser that be have possible to that for variables is remains environmental habitat it other precipitation, suitable as and of drastically temperature range for winter habitats the of on suitable that dent niche As first, the explanations. at several satisfies contradictory, propose that seems habitat it pennsylvanica more distributions Although P. be predicted scenarios. to The change appears collected. climate there was comparison, data In climatic and that occurrence indicating that time the for at change climate that mata suggesting data our .pennsylvanica P. pce r tl epnigt eetciai vn,o aearaybgnrsodn orecent to responding begun already have or event, climatic recent a to responding still are species and .pennsylvanica P. .pennsylvanica P. sntpeitdt hf otwr nrsos ofrcse niomna odtos we conditions, environmental forecasted to response in northward shift to predicted not is niae atclrypoietvrainaon hsseis ag de,possibly edges, range species’ this around variation prominent particularly indicates .pennsylvanica P. ugssta ieetbolmtcvralsatt ii ags ee we Here, ranges. limit to act variables bioclimatic different that suggests sna t lmtcotmm(ruo&Pasn 05 ucisn 1957). Hutchinson, 2005; Pearson, & (Araujo optimum climatic its near is le,Ry hms 01.Vraiiyi epne odifferent to responses in Variability 2011). Thomas, & Roy, uller, ¨ a eaieyrsrce ag,i spsil htboth that possible is it range, restricted relatively a has 6 .pennsylvanica P. .americana P. r rdce ob togydepen- strongly be to predicted are .americana P. and .pennsylvanica P. ie different given Phy- P. . Posted on Authorea 18 May 2020 — CC-BY 4.0 — https://doi.org/10.22541/au.158981471.19903295 — This a preprint and has not been peer reviewed. Data may be preliminary. otiuin hl I3(stemlt)hdtelretpruainiprac.FrP pennsylvanica, P. 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Errors models selection 2008b, distribution Rowe, temperature-dependent species & and in Rodd, errors fluctuating Punzalan, Spatial demonstrating 2008a; al., studies et ecology of temperature (Punzalan thermal series of on bugs literature importance a ambush of the body including work, extensive present insects, an challenges the with in to consistent of restricting is scope pointing conditions mechanism the environmental possibly fluctuating beyond a and quarter, is as mechanisms coldest specific eggs as the of of temperature of identification of overwintering of distribution temperature variability the mean The and in the distribution. mean upon identified geographic dependent also of particularly analyses exclusion). selection, determinants our when (i.e., severe important precipitation, occurrences summer, of potentially predicting for the source in results (during the important the precipitation as be Mirroring in present) can variability are later environment recovered winter bugs responses invoked the also adult plastic also in analyses and phenotypically has juvenile our fluctuations triggering flooding 1976) why be strong for to (Mason, explaining to period organisms, vulnerability species likely possibly important many potentially one are an For summer, in as to life. work of stage translate in winter egg end Previous might in the the spring. precipitation dormant during at following are of conditions material the species amount plant in both on the inundation of 1984). laid and or Wade, eggs are ground, & when which (Arnold the period eggs, stage near the life period, to to this specific corresponds During be quarter both diapause. may driest of factors the limits abiotic example, by abiotic For mediated the selection of natural determinant that important bility an as whereby precipitation species, implicate consistently analyses Our .pennsylvanica P. .americana P. Phymata .americana P. . niiul as individuals and .pennsylvanica P. ihig nrdand red in sightings .americana P. 7 aedffrn pia u eut ihih h possi- the highlight results Our optima. different have rvc es) si a euti emnl robust seemingly in result may it as versa), vice or .pennsylvanica P. .pennsylvanica P. osuhr aiue.Although latitudes. southern to ihig nbu;ti includes this blue; in sightings .pennsylvanica P. was Posted on Authorea 18 May 2020 — CC-BY 4.0 — https://doi.org/10.22541/au.158981471.19903295 — This a preprint and has not been peer reviewed. Data may be preliminary. iue1 oain fP mrcn ihig nrdadP enyvnc ihig nbu;ti includes this blue; 1864-2018. in from sightings frame pennsylvanica time P. a and spans red and in data science sightings citizen americana and P. museum of both Locations 1. Figure and pennsylvanica, P. for suitable that taxa. only locations both (top are for indicates RCP2.6 that suitable Red at locations are right). indicates 2050 that (bottom blue in locations RCP8.5 americana, pennsylvanica indicates P. and P. purple for left), and to suitable (bottom americana comparison only P. RCP6.0 in are right), of trajectories (top distributions RCP4.5 RCP of left), overlap different Projected under 4. habitat Figure suitable suitable. of becoming habitats change distributions. unsuitable of predicted previously percentage current indicate The red 2070 becoming and habitats 1. and predicted suitable blue 2050 Table depicts previously of for indicate row shade red projected top darker and shows The blue the row of blue. and shades third in unsuitable, lighter and the pennsylvanica second with P. RCP8.5, The and at points. respectively red with Tem- in distributions (Mean americana (1970-2000) P. BIO11 importance. current of and permutation Distributions contribution, largest 3. percent the Figure largest had Quarter) the Coldest had the Quarter) of Driest perature the of (Precipitation BIO17 8 Posted on Authorea 18 May 2020 — CC-BY 4.0 — https://doi.org/10.22541/au.158981471.19903295 — This a preprint and has not been peer reviewed. Data may be preliminary. al .Tepretg fcag fsial aia ne ieetrpeettv ocnrto pathway concentration representative distributions. different predicted under current habitat to suitable comparison of in change habitats suitable trajectories of unsuitable (RCP) previously percentage previously indicate The indicate for red red 1. and Table and projected blue blue shows of of row predicted shade shades depicts third darker lighter suitable. row the and the becoming top and with second The unsuitable, RCP8.5, The becoming blue. at habitats in respectively points. pennsylvanica 2070 P. occurrence and and with 2050 red in distributions americana (1970-2000) P. current of Distributions 3. Figure (Mean importance. BIO11 permutation and largest contribution, while the percent pennsyl- had runs, P. largest Quarter) For the MAXENT Coldest had importance. largest replicate the Quarter) permutation the Driest of 10 had largest Temperature the Seasonality) the the of respec- (Precipitation (Precipitation had over strongest BIO17 BIO15 (Isothermality) averaged their vanica, americana, BIO3 to P. response while For (bottom) contribution, mean deviation. pennsylvanica percent P. the standard one and indicates indicates (top) Red blue americana P. of predictors. curves tive Response 2. Figure .aeiaa25 .%+12 .%+2.3% + 2.7% + 1.2% + 1.7% + 2050 americana P. .pnslaia25 .%0+08 0.4% + 0.8% + 0 0.5% + 2050 pennsylvanica P. erRP. C45RP. RCP8.5 RCP6.0 RCP4.5 RCP2.6 Year 00–04 .%00 0 0.7% + 0.4% – 2.0% + 0.4% 1.1% – + 2070 1.6% + 2070 9 Posted on Authorea 18 May 2020 — CC-BY 4.0 — https://doi.org/10.22541/au.158981471.19903295 — This a preprint and has not been peer reviewed. 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