From 3ccb9ffd558aaeb1d285cb389c64fe36cb5e7010 Mon Sep 17 00:00:00 2001 From: embruna Date: Mon, 2 Dec 2024 16:47:43 -0500 Subject: [PATCH] added methods --- docs/ipm_comparison_ms.pdf | Bin 1706315 -> 1714319 bytes docs/ipm_comparison_ms.qmd | 98 +++++++--------- docs/lagged-ipms-ms.bib | 228 ++++++++++++++++++++++++++++++++----- 3 files changed, 245 insertions(+), 81 deletions(-) diff --git a/docs/ipm_comparison_ms.pdf b/docs/ipm_comparison_ms.pdf index 9572de9e6faeaae0eaa1d63d1d588e55956b208a..388572b8e4a67466db2ca68cbe969e382a55990e 100644 GIT binary patch delta 60524 zcmZUZQ+J>Z&}HMKV_O~DcG9tJ+wM4dI<{?fY}>YNCmq|#ylZAIzFBj3en72SwRfF~ z1k2bt%Qy~ziJL<}0M^CT+04ie)?+uanUkPYhpI2Q{J5)buE|*%TTq;28aOmDLNK*FGdw-~p?2a^jk40yJqfDY&;ttJNd zhu7L6i85S4PHKN^xwfJ%7OfOwZIJ1mJ5JxH#qRT1AGY5>VQfzj{1F=j=P?1Q(c|Kb-cXH7ebx;^Ie4_5G8Dq~y~jjNa}68P}N!1NGt z=KwIChdmCjf7z>!r$6WjJzqv8v;4%mOIXNhO0M3*rzXShRE!G4B|YJOL^E1(41J5P z-hvwe#B1>HRg{%ediF@W` z`hYD^Zq2hw-l*$DVqVUjB~ja*)prp-JQLgc!lwd`+@<+QnqEcw@t@P`70kx>6O0GX zGpm7{R?hJR;{>=J(~qV>*DKHBp9TK=R#a+Jab9M3jE&qgyE{269Bb$j2U+BDGb6kA zXuJNj@?N-2TX?iW=j}>p%Xom4!oH2!By|-tkwwJQY(=2X^?O^TV$;du=NV03ZdY!N zIGzI7ZThZhd;})Lk!4DOK;>o?3I3SwdtUYLgh(a%&?lE)Apbfig)RJ zn@{+(CQClSeAVQzhuL`)`H>Twx@|TO567VV_MlA5S-)N9j8eZPbB&&hbb_BTfF`1K zJ_j|T2O+}tCrj3K|Fx8ZAmGqd)GMTA;-q%Rn)sYci);O- zpK&a!9Z${AhIoiZFi_Tb6f%H~nU(#2E}A0U zu^6&8gsykmJ7^m5cQCRy!(NkKPH1>J}ylz4N3*c;DJ!7PP#GCuXhk&<<0*x%yY1@QMO1Y+S3EYDSDSlW$+7CRF58UkCpLT@}Wb(?@BWd>k5o{6iy3od_fU z%ibXYz22-2`J(CqE|$D5%!)Q3_9SE&;mA4b+T^WOmdQPWdBkv{JX07cKfulpK*UVh zhJrsTna|C);tG>(iqdl0gYeSj3cPJ_EUs1&?^zo1iDy^?%R4vDEi?}BvZ|I-$|O+} z=T+O$M)qf*EB_JMWe5jx|K}`MMs{XMi|Vt3K1}b6u%R09^{>iQW(PXTJm4@yEvfMK zV^#1nhRX*$0}dE-N%1Pr$9n1mcH#)@Z_~C9`am7RJ#T(|V(bh-&9qr(vuRVt7O3E$ zUlp=fs^J@VhH_CEve_@ps+3mHxbo-Z_Wb+&5uz?!3n%e4a@Irk1CKOxMgpNmK5#r?tGC1u1lRCqj=nGJji~vXFf;Nxf7mDex zLDbo*AHPO3*-ZUd&dp;6Dt1(KV7f<$Z7G_OCg0*p`1IWy+jC~{|~hpH?as?{s$5IvH$hlQ-3KdV+eI`{@8ET9Nv0q5qE5?_<_o5 z@Sk=mC+ZN3hI%Yqffa^yV1YcxawWv^eMr}ywdd`mSHbkhmDiV)Y7eFHM&y2O4BfQUpR88N~_`y$F}bi#&~itKP=#F+jHe*ZZ0 zUQ_fIh0L8kdG?4HK&(*S@iJ$A1KaF%oE;($XBzO}{lokc6?8F&W<^xf2cXReq}~b^ z&a0bq5(T%+zi?^}K{gZu7(D(S5jG!*}erSsxhQhIuoM;6_;A7)Pdgvm< zSgiicx`Gh>$3H_d=5Y4rQe&3hlOUJe<4x?~AHkf-nWCcgD_qn2M5Bc-UAuWlA1yRiu;2Ut3s_5(%!^?ey6nzuw=w{^T?ZZg3ieg&I_hWv1_SD z81(7Qu}-`OWtCf0Ej!E&7WT4q3JC>?3xX>3^L=i1`Php_)NoP3XTx?3at(E|O4(=y z5%yr}7{YjUx{JreSG5~Z z0am$u2?-x3zi@CjvpvchhN3Z%8(}q}nSpCCV2_avC-LjCMQ!5N%0$)I#G)DU>Tv}g zV;>KHv!LyinWxAiu!rbHSOGtpub3`4ZJMC~xXxr{WhrSAB${JJ`gtD@?dPBN-fNoG zPK+8*;mA_iCpvU!n{q?$>hIA6(l)g#5{|xY0}~68n30pBwQT0Ln%!xMQth;>%7j>e z+E6bS+XYJGf;i!Fd}Z!p$8$09`Efk-q^J-v)hkzY?sa8JH1!EK0eSN0$YwQetUD+M zfT3|Jo$`VYU`9=aDc%vz@e7f(*cA)v6$l%Qu~1{h{u!VBq)F(k_Zw>eehp5)A? z9SwYB`pF@_5^)HZ2MR3JtC~6~MfiI}i z$2p>m!#R`m$DbD-Uo3UwznEI0;fsooI*dt}L7CckQFA(YEL*YFo#C{YbC2EtK)4rH zhbYO&#eT5IoJ;lqiZ;UvL-q{m1TMfCcNgcqHHor}pTwt*D|b`IupBD6z19hjs+Gwp zb~>-QD*zdxUPo>3&8tgLa>u~CfLs3yMe34;c?#1fv}OP~2GL1dvS$J0|wvs>?VCZ0Tl1|)_ zV0N~U|F~FBbF=1O*)Hc|00Z6_BWq zv~>wuzmVXRHrvhxQysun2^!3iK7})49O(20%Yjb|J`U%;euErl5zef{4=^CD2y3*Z zwZnNE+WJc~7rt>n)7WMi+5bR^j^A6J9 z%Q3~B3{K23 z64NCl=PBF3M6w!1=FnY8L3WPy1Qa&wpKdlQ)Wc8b?v^5pso?qo4DW2*Io)kx)^6H` ze8x#dsEdefC=T4C>HAmk@(P~5=o13hfk`J^$>z8kn|5O|$Y3qH@meGqQ^CC{-ka4^iVQs^XyNlS8kNq4(h8MNkOJ z*&`L+#=F@~J$o;}i4kb4>I&y@{fDr3rs>t2bS1skkI;VHyZuhaTW#|?4oE5j`GhY7 z(SCti(~JYPa{j_4n)TTVp}(F68L*Nm&KBmP%G-Hc38VAzT|IKktQmq4^5;QS61TQbvsb@qMfwLOv9%L+8q!nfar+rLMuiWoQtC$hK0gy#kv_`V zMTcxUxNWF|zH+$fP3tdu&r7wpDcPAaFsb8~HKwF)iTKoAS7HX{ivu z*uI+FkKICAo|rqEVmq0Pw6Mt(gt3=z&>i+uV8Yr^IUd*B+*=Uef>rEEGf2vP>&=te z!&w2V-^k+~VcLSA>wK%(8oXY*ptgthOLkuvy~7=-Wk{=(RU{k&B|24yTO=4NV$q&x#wGqb2V} zGH;TKkYw3baA;n@;PpcFd|-8mC!wzE7S_@_&=Q8kmiP>)5a_w5jH&hGSGT>`X+i

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frameworks used to study demography and population dynamics. Their flexibility, in concert with a rapidly growing suite of software, data, and other resources [@ellnerDatadrivenModellingStructured2016; @levinIpmrFlexibleImplementation2021; @salguero-gomezCOMPADREPlantMatrix2015], have facilitated their use to study a wide range of topics in ecology, evolution, and conservation [@ellnerDatadrivenModellingStructured2016; @morrisQuantitativeConservationBiology2002; @croneHowPlantEcologists2011]. Mathematical and statistical advances [e.g., @brooksStatisticalModelingPatterns2019; @williams2012avoiding]have rapidly expand the scope of questions and biological processes that can be investigated with these models [e.g., @reesEvolvingIntegralProjection2016; @ellnerDatadrivenModellingStructured2016]. Despite this progress, however, several important biological processes have proven challenging to incorporate in structured population models. In some cases this is because theoretical and analytical methods for doing so remain underdeveloped; in others this is because data with which to parametertize and assess alternative model structures are lacking [@ellnerDatadrivenModellingStructured2016; @metcalfStatisticalModellingAnnual2015].\ +Integral Projection Models (i.e., IPMs) are an important and widely used tool for studying demography and population dynamics [@ellnerIntegralProjectionModels2006; @reesBuildingIntegralProjection2014; @reesIntegralProjectionModels2009]. Their flexibility, in concert with a rapidly growing suite of software, data, and other resources [@ellnerDatadrivenModellingStructured2016; @levinIpmrFlexibleImplementation2021; @salguero-gomezCOMPADREPlantMatrix2015], have facilitated their use to study a wide range of topics in ecology, evolution, and conservation [@ellnerDatadrivenModellingStructured2016; @morrisQuantitativeConservationBiology2002; @croneHowPlantEcologists2011]. Mathematical and statistical advances [e.g., @brooksStatisticalModelingPatterns2019; @williams2012avoiding]have rapidly expand the scope of questions and biological processes that can be investigated with these models [e.g., @reesEvolvingIntegralProjection2016; @ellnerDatadrivenModellingStructured2016]. Despite this progress, however, several important biological processes have proven challenging to incorporate in structured population models. In some cases this is because theoretical and analytical methods for doing so remain underdeveloped; in others this is because data with which to parametertize and assess alternative model structures are lacking [@ellnerDatadrivenModellingStructured2016; @metcalfStatisticalModellingAnnual2015].\ -One of these biological processes is *Delayed Life-history Events* (i.e., DLHEs), also known as *Lagged Effects* [@beckermanPopulationDynamicConsequences2002].Lagged effects are those in which the demographic vital rates observed in a given year are influenced -- or even determined by -- past environmental conditions. For instance, environmental conditions during juvenile development can shape the expression of traits (e.g., defensive spikes on *Daphnia*) that determine adult survival. Alternatively, the physiological mechanisms responsible for a vital rate can take an extended period of time to complete [@eversLaggedDormantSeason2021]; for example, flowering bud formation may be initiated several months before flowers appear [@crileyYearProductionHigh1994]. Vital rates can even be influenced by environmental conditions during the parental life-cycle or historical trade-offs between vital rates (e.g., delayed costs of reproduction, competition-colonization trade-offs). Although these lagged effects could potentially have major consequences for population dynamics [@beckermanPopulationDynamicConsequences2002], their impacts remain poorly understood for two primary reasons. The first is limited data - parametrizing models requires long-term data on lagged effects and their potential drivers [@metcalfStatisticalModellingAnnual2015], and these studies can be challenging to design and maintain [@kussEvolutionaryDemographyLonglived2008a]. The second is challenge is technical - incorporating complex biological processes in demographic models can render them less tractable.\ +One of these biological processes is *Delayed Life-history Events* (i.e., DLHEs), also known as *Lagged Effects* [@beckermanPopulationDynamicConsequences2002]. Lagged effects are those in which the demographic vital rates observed in a given year are influenced -- or even determined by -- past environmental conditions. For instance, environmental conditions during juvenile development can shape the expression of traits (e.g., defensive spikes on *Daphnia*) that determine adult survival. Alternatively, the physiological mechanisms responsible for a vital rate can take an extended period of time to complete [@eversLaggedDormantSeason2021]; for example, flowering bud formation may be initiated several months before flowers appear [@crileyYearProductionHigh1994]. Vital rates can even be influenced by environmental conditions during the parental life-cycle or historical trade-offs between vital rates (e.g., delayed costs of reproduction, competition-colonization trade-offs).\ -Despite these challenges, several recent studies have found there can be large delayed effects of environmental conditions (e.g., climate) on demographic vital rates [@eversLaggedDormantSeason2021; @scottDelayedEffectsClimate2022]. It remains unclear, however, if including such lagged effects will significantly alter the results of demographic models. Using a decade of survey and climate data, we assessed the effects of precipitation extremes on the demographic vital rates of the Amazonian understory herb *Heliconia acuminata* (Heliconiaceae). We found that the effects of climate on vital rates could be delayed up to 36 months, with the presence and duration of these effects differing by vital rate and habitat type (i.e., continuous forest, forest fragments). Here we used these data to parameterize three different classes of Intgral Projection Models (IPMs): a deterministic IPM, a stochastic IPM, and a stochastic IPM with lagged effects of SPEI on vital rates. We then evaluated how model choice influenced projections of population growth rate (i.e., $\lambda$) and structure? Based on previous studies [@brunaArePlantPopulations2003; @brunaDemographicEffectsHabitat2005; @brunaExperimentalAssessmentHeliconia2002; @brunaHabitatFragmentationDemographic2002] and demographic theory [@caswell2001; @tuljapurkarPopulationDynamicsVariable1990] we predicted that:\ +Although such lagged effects could potentially have major consequences for population dynamics [@beckermanPopulationDynamicConsequences2002], their their demographic impacts remain poorly understood [but see @molowny-horasChangesNaturalDynamics2017; @tenhumbergTimelaggedEffectsWeather2018; @williamsLifeHistoryEvolution2015]. There are two primary reasons for this limited understanding. The first is a lack of empirical data [sensu @eversLaggedDormantSeason2021]. Detecting lagged effects requires long-term data on both the putative lagged effects and their potential drivers [@metcalfStatisticalModellingAnnual2015], and studies to detect them can be challenging to design and maintain [@kussEvolutionaryDemographyLonglived2008a]. The second is challenge is technical - incorporating complex biological processes such as lagged effects in demographic models can render the models less tractable. \ -(i) $\lambda$ would be higher in continuous forest forest fragments regardless of model type, +We used a decade of survey and climate data to assess the effects of precipitation extremes on the demographic vital rates of the Amazonian understory herb *Heliconia acuminata* [@scottDelayedEffectsClimate2022]. Our analyses revealed that the effects of climate on vital rates could be delayed up to 36 months, and that the presence and duration of these effects could differ by vital rate and habitat type (i.e., continuous forest vs. forest fragments). Here we investigate how including lagged effects in Integral Projection Models influences projections of population growth rate (i.e., $\lambda$) and structure. To do so we parameterized and compared three different classes of Integral Projection Models: a deterministic IPM, a stochastic IPM, and a stochastic IPM with lagged effects of SPEI on vital rates. Based on previous studies [@brunaArePlantPopulations2003; @brunaDemographicEffectsHabitat2005; @brunaExperimentalAssessmentHeliconia2002; @brunaHabitatFragmentationDemographic2002] and demographic theory [@caswell2001; @tuljapurkarPopulationDynamicsVariable1990] we predicted that:\ + +(i) $\lambda$ would be higher in forest than fragments regardless of model type, (ii) that projections of $\lambda$ from deterministic models would be higher than those of stochastic models, (iii) that $\lambda$ would be lowest for models including lagged effects, and -(iv) populations would be more skewed towards pre-reproductive size classes in fragments that forest, regadless of wether models included stochaticity or lagged effects. +(iv) populations would be more skewed towards pre-reproductive size classes in fragments that forest, regardless of whether models included stochasticity or lagged effects. ## Methods ### *Study System and Demographic Data* - - -> Overview of the *Heliconia* project - -### *Construction of Integral Projection Models* +*Heliconia acuminata* (Heliconiaceae) is a perennial, self-incompatible monocot [@kressDiversityDistributionHeliconia1990] that is distributed throughout much of the Amazon basin [@kressDiversityDistributionHeliconia1990]. While some *Heliconia* species grow in large aggregations on roadsides, gaps, and in other disturbed habitats, others - including *H. acuminata* - grow primarily in the forest understory [@kressSelfincompatibilitySystemsCentral1983; @ribeiroInfluencePostclearingTreatment2010]. Understory *Heliconia* species typically produce fewer flowers and are pollinated by traplining hummingbirds [@stoufferForestFragmentationSeasonal1996; @brunaHeliconiaAcuminataReproductive2004].\ -```{=html} - -``` +### *Construction of Integral Projection Models* In preliminary investigation, we found that the survival and growth of plants was better explained by treating seedlings and mature plants separately. Seedlings are physiologically different from small plants because they necessarily lack the underground reserves (of carbohydrates and meristems) that a small, mature plant may have. Therefore, we used general IPMs to model population dynamics with seedlings treated as a separate discreet class not structured by size. General IPMs allow for combinations of continuous and discrete states and transitions between them [@ellnerDatadrivenModellingStructured2016].\ @@ -240,6 +235,38 @@ This workflow was managed using the `targets` R package [@targets] which also al ## Results & Discussion +```{r } +meta_df <- + tar_meta(store = here("_targets")) %>% + select(name, size, bytes, time, seconds) %>% + filter(str_detect(name, "^ipm_\\w+_[cf]{2}$")) %>% + mutate(minutes = seconds / 60, + hours = minutes / 60) + +meta_df_tbl<-meta_df %>% + separate(name, into = c("trash", "IPM", "Habitat")) %>% + select(-trash) %>% + select(IPM, Habitat, minutes) %>% + group_by(IPM) %>% +mutate(IPM = str_replace_all( + IPM, + c( + "det" = "det", # Deterministic", + "stoch" = "sk", # Stochastic, kernel-resampled + "dlnm" = "sp" # Stochastic, parameter-resampled + ))) %>% + summarize(mean_time_min = round(mean(minutes), 2)) %>% + rename("IPM Type" = IPM) %>% + rename("mean time (min.)" = mean_time_min) + +det_time<-meta_df_tbl %>% filter(`IPM Type`=="det") +det_time<-as.numeric(det_time[1,2]) +sker_time<-meta_df_tbl %>% filter(`IPM Type`=="sk") +sker_time<-as.numeric(sker_time[1,2]) +spar_time<-meta_df_tbl %>% filter(`IPM Type`=="sp") +spar_time<-as.numeric(spar_time[1,2]) +``` + 1. For all vital rates estimated using the long term demographic dataset, the DLNM model fit the best (dAIC = 0) followed by the model with a random effect of year, followed by the deterministic model (@tbl-aic).\ @@ -247,7 +274,7 @@ This workflow was managed using the `targets` R package [@targets] which also al 3. We found that the the choice of Integral Projection Model didn't change the relative ranking of lambda in Continous Forest and Fragments.\ -4. The time to iterate the DLNM models is much higher than than deterministic and kernel-resampled.\ +4. DLNM models take much, much longer to iterate: while the Deterministic and Kernel-resampled stochastic models took \~`r det_time` and \~`r sker_time` min to iterate (respectively), the Parameter-resampled stochastic models with lagged effects took \~`r spar_time` min.\ 5. The greater use of computational resources is likely a result of `predict()` being much slower for GAMs with 2D smooths because the number of knots is much higher compared to the GAMs used for the vital rates models in the determinsitic and kernel-resampled IPMs.\ @@ -368,43 +395,6 @@ pandoc.table( \newpage -```{r tbl-time, results='asis'} -#| label: tbl-time -#| tbl-cap: "Figure caption to be written" -meta_df <- - tar_meta(store = here("_targets")) %>% - select(name, size, bytes, time, seconds) %>% - filter(str_detect(name, "^ipm_\\w+_[cf]{2}$")) %>% - mutate(minutes = seconds / 60, - hours = minutes / 60) - -meta_df_tbl<-meta_df %>% - separate(name, into = c("trash", "IPM", "Habitat")) %>% - select(-trash) %>% - select(IPM, Habitat, minutes) %>% - group_by(IPM) %>% -mutate(IPM = str_replace_all( - IPM, - c( - "det" = "Deterministic", - "stoch" = "Stochastic, kernel-resampled", - "dlnm" = "Stochastic, parameter-resampled" - ))) %>% - summarize(mean_time_min = round(mean(minutes), 2)) %>% - rename("IPM Type" = IPM) %>% - rename("mean time (min.)" = mean_time_min) - -kbl(meta_df_tbl, - booktabs = T, - align = "lc") %>% - kable_styling(full_width = F, - position = "center") - - -``` - -\newpage - ```{r tbl-lambdas, results='asis'} #| label: tbl-lambdas #| tbl-cap: "Population growth rates for continuous forest (CF) and forest fragments (FF) under different kinds of IPMs with bootstrapped, bias-corrected, 95% confidence intervals." diff --git a/docs/lagged-ipms-ms.bib b/docs/lagged-ipms-ms.bib index b3704c0..8c93567 100644 --- a/docs/lagged-ipms-ms.bib +++ b/docs/lagged-ipms-ms.bib @@ -1,4 +1,29 @@ +@article{brunaHeliconiaAcuminataReproductive2004, + title = {\textit{{Heliconia} acuminata} reproductive success is independent of local floral density}, + volume = {34}, + issn = {0044-5967}, + url = {https://doi.org/10.1590/S0044-59672004000300012}, + doi = {10.1590/S0044-59672004000300012}, + language = {English}, + number = {3}, + journal = {Acta Amazonica}, + author = {Bruna, Emilio M. and Kress, W. John and Marques, Francisco and Silva, Osmaildo Ferreira da}, + year = {2004}, + note = {tex.ids= brunaHeliconiaAcuminataReproductive2004a, brunaHeliconiaAcuminataReproductive2004b, brunaHeliconiaAcuminataReproductive2004c}, + pages = {467--471}, + file = {Bruna et al. - 2004 - Heliconia acuminata reproductive success is.pdf:/Users/emiliobruna/Zotero/storage/QVITFVK4/Bruna et al. - 2004 - Heliconia acuminata reproductive success is.pdf:application/pdf}, +} + +@book{bierregaardLessonsAmazoniaEcology2001, + address = {New Haven}, + title = {Lessons from {Amazonia}: {The} ecology and conservation of a fragmented forest}, + isbn = {0-300-08483-8}, + publisher = {Yale University Press}, + editor = {Bierregaard, R. O. and Gascon, C. and Lovejoy, T. E. and Mesquita, R.}, + year = {2001}, +} + @article{brooksStatisticalModelingPatterns2019, title = {Statistical modeling of patterns in annual reproductive rates}, volume = {100}, @@ -12,6 +37,20 @@ @article{brooksStatisticalModelingPatterns2019 keywords = {conway-maxwell-poisson fecundity generalized poisson heliconia acuminata oreothlypis celata overdispersion regression underdispersion zero-inflation habitat fragmentation life-history count data growth}, } +@article{brunaSeedGerminationRainforest1999, + title = {Seed germination in rainforest fragments}, + volume = {402}, + url = {https://doi.org/10.1038/45963}, + doi = {10.1038/45963}, + number = {6758}, + journal = {Nature}, + author = {Bruna, E. M.}, + year = {1999}, + keywords = {Musaceae Angiosperms Monocots Spermatophytes Vascular Plants Demography Habitat Fragmentation Habitat Loss Seed Dispersal Seed Germination Seed Production Seedling Emergence Note}, + pages = {139}, + file = {Bruna - 1999 - Seed germination in rainforest fragments.pdf:/Users/emiliobruna/Zotero/storage/QNB7KJXB/Bruna - 1999 - Seed germination in rainforest fragments.pdf:application/pdf}, +} + @article{brunaHabitatFragmentationDemographic2002, title = {Habitat fragmentation and the demographic structure of an {Amazonian} understory herb (\textit{{Heliconia} acuminata})}, volume = {16}, @@ -56,6 +95,45 @@ @article{ellnerIntegralProjectionModels2006 pages = {410--428}, } +@article{kressDiversityDistributionHeliconia1990, + title = {The diversity and distribution of \textit{{Heliconia}} ({Heliconiaceae}) in {Brazil}}, + volume = {4}, + doi = {10.1590/S0102-33061990000100011}, + number = {1}, + journal = {Acta Botanica Brasileira}, + author = {Kress, J.}, + year = {1990}, + pages = {159--167}, +} + +@article{kressSelfincompatibilitySystemsCentral1983, + title = {Self-incompatibility systems in {Central} {American} \textit{{Heliconia}}}, + volume = {37}, + url = {https://doi.org/10.2307/2407915}, + doi = {10.2307/2407915}, + number = {4}, + journal = {Evolution}, + author = {Kress, W. J.}, + year = {1983}, + pages = {735--744}, +} + +@article{molowny-horasChangesNaturalDynamics2017, + title = {Changes in the natural dynamics of \textit{{Nothofagus} dombeyi} forests: population modeling with increasing drought frequencies}, + volume = {8}, + url = {https://doi.org/10.1002/ecs2.1708}, + doi = {10.1002/ecs2.1708}, + abstract = {Drought-induced episodes of tree mortality can determine forest dynamics and structure, particularly in forests dominated by single species. Short-and mid-term climate projections indicate that strong changes in annual precipitation may strike more often in northern Patagonia. Data for recruitment, growth, and survival of Nothofagus dombeyi tree individuals were collected at several sites across the Nahuel Huapi National Park in Argentina. We combined mathematically all these different demographic stages into an Integral Projection Model to simulate 100-yr projections of simulated stand structure under different frequencies of extreme drought episodes. We projected total basal area and the number of individuals for three different initial stand types (i.e., young, medium, and old) and for varying drought frequencies (i.e., from 1 to 5 drought events every 100 years). Recruitment into the dbh {\textgreater}= 10 cm size class under normal conditions (i.e., without drought) was higher than under episodic drought conditions. In addition, survival under normal conditions was higher than under drought conditions, especially for small trees. Differences in growth were also important, with trees growing more vigorously under normal than under drought conditions. Our simulations predicted that N. dombeyi populations would experience a reduction in tree density in the mid-term if, as predicted by the IPCC projections, the frequency of future drought events increased. The simulations also showed that in those cases, young stands should suffer the most. Drought-mediated changes may induce a decline in the development of N. dombeyi forests in the mid-and long term by a drastic reduction in tree density.}, + number = {3}, + journal = {Ecosphere}, + author = {Molowny-Horas, R. and Suarez, M. L. and Lloret, F.}, + year = {2017}, + note = {tex.ids= molowny-horasChangesNaturalDynamics2017a}, + keywords = {austral forests, climate change, drought episodes, forest dieback, Nothofagus dombeyi, population dynamics modeling}, + pages = {1--17}, + file = {Molowny-Horas et al. - 2017 - Changes in the natural dynamics of Nothofagus domb.pdf:/Users/emiliobruna/Zotero/storage/C7MJXZF3/Molowny-Horas et al. - 2017 - Changes in the natural dynamics of Nothofagus domb.pdf:application/pdf;Snapshot:/Users/emiliobruna/Zotero/storage/N3IEM7GW/ecs2.html:text/html}, +} + @book{morrisQuantitativeConservationBiology2002, address = {Sunderland, MA}, title = {Quantitative conservation biology: theory and practice of population viability analysis.}, @@ -106,6 +184,103 @@ @article{reesEvolvingIntegralProjection2016 file = {Rees and Ellner - 2016 - Evolving integral projection models evolutionary .pdf:/Users/emiliobruna/Zotero/storage/YQ9JD9QC/Rees and Ellner - 2016 - Evolving integral projection models evolutionary .pdf:application/pdf}, } +@article{ribeiroInfluencePostclearingTreatment2010, + title = {Influence of post-clearing treatment on the recovery of herbaceous plant communities in {Amazonian} secondary forests}, + volume = {18}, + doi = {10.1111/j.1526-100X.2010.00715.x}, + journal = {Restoration Ecology}, + author = {Ribeiro, Maria Beatriz Nogueira and Bruna, E. M. and Mantovani, W.}, + year = {2010}, + keywords = {central amazonia edge effect fire land-use history regeneration species diversity tropical rain-forest species richness neotropical forests abandoned pastures degraded lands leaf-litter seed rain regeneration succession understory}, + pages = {50--58}, +} + +@article{stoufferEffectsForestFragmentation1995, + title = {Effects of forest fragmentation on understory hummingbirds in {Amazonian} {Brazil}}, + volume = {9}, + url = {https://www.jstor.org/stable/2387046}, + number = {5}, + journal = {Conservation Biology}, + author = {Stouffer, Philip C. and Bierregaard, Richard O.}, + year = {1995}, + keywords = {❓ Multiple DOI, Apodiformes 00512 (General Biology–Conservation, General and Systematic Zoology–Aves) Animals Chordates Vertebrates Nonhuman vertebrates Birds Research Article Conservation Patch Size, Resource Management) 07518 (Ecology Environmental Biology–Wildlife Management-Terrestrial) 53500 (Forestry and Forest Products) 62518 (Chordata}, + pages = {1085--1094}, + file = {Stouffer and Bierregaard - 1995 - Effects of forest fragmentation on understory humm.pdf:/Users/emiliobruna/Zotero/storage/2EQ8NYVB/Stouffer and Bierregaard - 1995 - Effects of forest fragmentation on understory humm.pdf:application/pdf}, +} + +@article{stoufferForestFragmentationSeasonal1996, + title = {Forest fragmentation and seasonal patterns of hummingbird abundance in {Amazonian} {Brazil}}, + volume = {4}, + number = {1}, + journal = {Ararajuba}, + author = {Stouffer, Philip C. and Bierregaard, Richard O.}, + year = {1996}, + keywords = {⛔ No DOI found}, + pages = {9--14}, +} + +@article{tenhumbergTimelaggedEffectsWeather2018, + title = {Time-lagged effects of weather on plant demography: {Drought} and \textit{{Astragalus} scaphoides}}, + volume = {99}, + issn = {00129658}, + shorttitle = {Time-lagged effects of weather on plant demography}, + doi = {10.1002/ecy.2163}, + abstract = {Temperature and precipitation determine the conditions where plant species can occur. Despite their significance, to date, surprisingly few demographic field studies have considered the effects of abiotic drivers. This is problematic because anticipating the effect of global climate change on plant population viability requires understanding how weather variables affect population dynamics. One possible reason for omitting the effect of weather variables in demographic studies is the difficulty in detecting tight associations between vital rates and environmental drivers. In this paper, we applied Functional Linear Models (FLMs) to long-term demographic data of the perennial wildflower, Astragalus scaphoides, and explored sensitivity of the results to reduced amounts of data. We compared models of the effect of average temperature, total precipitation, or an integrated measure of drought intensity (standardized precipitation evapotranspiration index, SPEI), on plant vital rates. We found that transitions to flowering and recruitment in year t were highest if winter/spring of year t was wet (positive effect of SPEI). Counterintuitively, if the preceding spring of year t - 1 was wet, flowering probabilities were decreased (negative effect of SPEI). Survival of vegetative plants from t - 1 to t was also negatively affected by wet weather in the spring of year t - 1 and, for large plants, even wet weather in the spring of t - 2 had a negative effect. We assessed the integrated effect of all vital rates on life history performance by fitting FLMs to the asymptotic growth rate, log(t). Log(t) was highest if dry conditions in year t - 1 were followed by wet conditions in the year t. Overall, the positive effects of wet years exceeded their negative effects, suggesting that increasing frequency of drought conditions would reduce population viability of A.scaphoides. The drought signal weakened when reducing the number of monitoring years. Substituting space for time did not recover the weather signal, probably because the weather variables varied little between sites. We detected the SPEI signal when the analysis included data from two sites monitored over 20yr (2x20 observations), but not when analyzing data from four sites monitored over 10yr (4x10 observations).}, + language = {English}, + number = {4}, + journal = {Ecology}, + author = {Tenhumberg, B. and Crone, E. E. and Ramula, S. and Tyre, A. J.}, + year = {2018}, + note = {tex.ids= tenhumbergTimelaggedEffectsWeather2018a, tenhumbergTimelaggedEffectsWeather2018b}, + keywords = {carryover, carryover effects, climate-change, demography, detecting, detecting weather signals, deterioration, drivers, drought, dynamics, effects, environmental, environmental drivers, european, extinction, for, growth-rate, habitat, herb, higher-plants, integral, limitation, matrix, matrix models, models, perennial, plant, plant demography, pollen, population-models, projection, read, risk, signals, space, space for time substitution, SPEI, substitution, time, weather}, + pages = {915--925}, + file = {Snapshot:/Users/emiliobruna/Zotero/storage/5NMDSNQG/ecy.html:text/html;Tenhumberg et al. - 2018 - Time-lagged effects of weather on plant demography.pdf:/Users/emiliobruna/Zotero/storage/7ZTS98G7/Tenhumberg et al. - 2018 - Time-lagged effects of weather on plant demography.pdf:application/pdf}, +} + +@article{uriarteDisentanglingDriversReduced2011, + title = {Disentangling the drivers of reduced long-distance seed dispersal by birds in an experimentally fragmented landscape}, + volume = {92}, + url = {http://doi.wiley.com/10.1890/10-0709.1}, + doi = {10.1890/10-0709.1}, + language = {English}, + number = {4}, + journal = {Ecology}, + author = {Uriarte, María and Anciães, Marina and da Silva, Mariana T. B. and Rubim, Rubim and Johnson, Erik and Bruna, Emilio M.}, + year = {2011}, + note = {tex.ids= uriarteDisentanglingDriversReduced2011a}, + keywords = {amazonian rain forest dispersal kernels frugivory habitat fragmentation heliconia acuminata manakins manaus, brazil patch size spatially explicit model thrush tropical forest turdus albicollis endemic african tree habitat fragmentation avian frugivores forest fragmentation heliconia-acuminata understory herb recruitment patterns plant consequences}, + pages = {924--937}, + file = {Uriarte et al. - 2011 - Disentangling the drivers of reduced long-distance.pdf:/Users/emiliobruna/Zotero/storage/28GKVLZL/Uriarte et al. - 2011 - Disentangling the drivers of reduced long-distance.pdf:application/pdf}, +} + +@article{uriarteEffectsForestFragmentation2010, + title = {Effects of forest fragmentation on the seedling recruitment of a tropical herb: assessing seed vs. safe-site limitation}, + volume = {91}, + doi = {10.1890/09-0785.1}, + number = {5}, + journal = {Ecology}, + author = {Uriarte, M. and Bruna, E. M. and Rubim, P. and Anciaes, M. and Jonckheere, I.}, + year = {2010}, + keywords = {acuminata, amazonian, Amazonian, Amazonian forest, atlantic, distribution, forest, forests, Heliconia, Heliconia acuminata, heliconia-acuminata, herb, herbs, landscape, landscape modification, limitation, mesoscale, modification, neotropical, plant-populations, postagricultural, rain-forest, read, recruitment, safe-cite, safe-site limitation, seed, seed limitation, seedling, seedling recruitment, seedlings, shade, tolerance, tree, understory, woodland}, + pages = {1317--1328}, + file = {Uriarte et al. - 2010 - Effects of forest fragmentation on the seedling re.pdf:/Users/emiliobruna/Zotero/storage/LEFRVAI6/SYP6E5CQ.pdf:application/pdf}, +} + +@article{williamsLifeHistoryEvolution2015, + title = {Life history evolution under climate change and its influence on the population dynamics of a long-lived plant}, + volume = {103}, + url = {https://doi.org/10.1111/1365-2745.12369}, + doi = {10.1111/1365-2745.12369}, + number = {4}, + journal = {Journal of Ecology}, + author = {Williams, Jennifer L. and Jacquemyn, Hans and Ochocki, Brad M. and Brys, Rein and Miller, Tom E. X. and Shefferson, Richard}, + year = {2015}, + note = {tex.ids: williamsLifehistoryEvolutionClimate2015}, + keywords = {adaptive, costs, delay, delayed, dynamics, ecological, environments, evolution, evolutionarily, flowering, food-deceptive, growth, integral, interactions, model, orchid, perennial, plant, plant-climate, population, populations, projection, rapid, reproduction, reproductive, size, stable, stochastic, strategies, strategy, structured, variable}, + pages = {798--808}, + file = {Williams et al. - 2015 - Life history evolution under climate change and it.pdf:/Users/emiliobruna/Zotero/storage/TSRFBD9B/Williams et al. - 2015 - Life history evolution under climate change and it.pdf:application/pdf}, +} + @article{crileyYearProductionHigh1994, series = {New ornamental crops and the market for floricultural products}, title = {Year around production with high yields may be a possibility for \textit{{Heliconia} chartacea}}, @@ -134,7 +309,7 @@ @article{brunaArePlantPopulations2003 journal = {Ecology}, author = {Bruna, Emilio M.}, year = {2003}, - keywords = {Amazon, elasticity analyses, Heliconia acuminata, lambda, matrix models, plant demography, read, recruitment limitation, seed dispersal, source–sink}, + keywords = {seed dispersal, lambda, recruitment limitation, plant demography, Amazon, matrix models, Heliconia acuminata, read, elasticity analyses, source–sink}, pages = {932--947}, file = {Bruna - 2003 - Are plant populations in fragmented habitats recru.pdf:/Users/emiliobruna/Zotero/storage/HXXMTHNU/Bruna - 2003 - Are plant populations in fragmented habitats recru.pdf:application/pdf}, } @@ -155,7 +330,7 @@ @article{tuljapurkarStageAgeVariable2006 author = {Tuljapurkar, Shripad and Horvitz, Carol C.}, year = {2006}, note = {\_eprint: https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/0012-9658\%282006\%2987\%5B1497\%3AFSTAIV\%5D2.0.CO\%3B2}, - keywords = {age structure, Calathea, demography, life expectancy, Markov chains, stage structure, survivorship}, + keywords = {demography, stage structure, survivorship, Calathea, age structure, life expectancy, Markov chains}, pages = {1497--1509}, file = {Snapshot:/Users/emiliobruna/Zotero/storage/R5Q6SJ32/0012-9658(2006)87[1497FSTAIV]2.0.html:text/html;Tuljapurkar and Horvitz - 2006 - From Stage to Age in Variable Environments Life E.pdf:/Users/emiliobruna/Zotero/storage/7DXKXYCQ/Tuljapurkar and Horvitz - 2006 - From Stage to Age in Variable Environments Life E.pdf:application/pdf}, } @@ -197,7 +372,7 @@ @article{brunaDemographicEffectsHabitat2005 year = {2005}, note = {\_eprint: https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/04-1716 tex.ids= brunaDemographicConsequencesHabitat2005}, - keywords = {Amazon, deforestation, Heliconia acuminata, Heliconiaceae, lambda, life table response experiment, LTRE, matrix models, population growth rate, sensitivity analysis}, + keywords = {deforestation, lambda, Amazon, population growth rate, matrix models, Heliconia acuminata, Heliconiaceae, life table response experiment, LTRE, sensitivity analysis}, pages = {1816--1824}, file = {Bruna and Oli - 2005 - Demographic effects of habitat fragmentation on a .pdf:/Users/emiliobruna/Zotero/storage/X9GTB53N/Bruna and Oli - 2005 - Demographic effects of habitat fragmentation on a .pdf:application/pdf;Snapshot:/Users/emiliobruna/Zotero/storage/6UPQTTTU/04-1716.html:text/html}, } @@ -217,7 +392,7 @@ @article{brunaExperimentalAssessmentHeliconia2002 year = {2002}, note = {\_eprint: https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1046/j.1365-2745.2002.00707.x tex.ids= brunaExperimentalAssessmentHeliconia2002}, - keywords = {forest fragmentation, growth rates, Heliconiaceae, read, root : shoot ratio, understorey plants, understory plants, water stress}, + keywords = {forest fragmentation, water stress, read, growth rates, Heliconiaceae, root : shoot ratio, understory plants, understorey plants}, pages = {639--649}, file = {Bruna et al. - 2002 - Experimental assessment of Heliconia acuminata gro.pdf:/Users/emiliobruna/Zotero/storage/Z5QLRP2B/Bruna et al. - 2002 - Experimental assessment of Heliconia acuminata gro.pdf:application/pdf}, } @@ -377,7 +552,7 @@ @article{tuljapurkarManyGrowthRates2003 month = oct, year = {2003}, note = {Publisher: The University of Chicago Press}, - keywords = {Ardisia escallonioides, canopy‐gap forest dynamics, elasticity, hurricanes, norm of response, plant population biology, sensitivity, stochastic demography, temporal variation in demography}, + keywords = {elasticity, sensitivity, hurricanes, stochastic demography, Ardisia escallonioides, canopy‐gap forest dynamics, norm of response, plant population biology, temporal variation in demography}, pages = {489--502}, file = {Tuljapurkar et al. - 2003 - The Many Growth Rates and Elasticities of Populati.pdf:/Users/emiliobruna/Zotero/storage/KKBTHGMV/Tuljapurkar et al. - 2003 - The Many Growth Rates and Elasticities of Populati.pdf:application/pdf}, } @@ -904,27 +1079,6 @@ @article{brothertonImmediateLagEffects2019 file = {Full Text PDF:/Users/emiliobruna/Zotero/storage/HWCHJ27Y/Brotherton et al. - 2019 - Immediate and lag effects of hydrological change on floodplain grassland plants.pdf:application/pdf}, } -@article{tenhumbergTimelaggedEffectsWeather2018a, - title = {Time-lagged effects of weather on plant demography: drought and {Astragalus} scaphoides}, - volume = {99}, - copyright = {© 2018 The Authors Ecology published by Wiley Periodicals, Inc. on behalf of Ecological Society of America.}, - issn = {1939-9170}, - shorttitle = {Time-lagged effects of weather on plant demography}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ecy.2163}, - doi = {10.1002/ecy.2163}, - abstract = {Temperature and precipitation determine the conditions where plant species can occur. Despite their significance, to date, surprisingly few demographic field studies have considered the effects of abiotic drivers. This is problematic because anticipating the effect of global climate change on plant population viability requires understanding how weather variables affect population dynamics. One possible reason for omitting the effect of weather variables in demographic studies is the difficulty in detecting tight associations between vital rates and environmental drivers. In this paper, we applied Functional Linear Models (FLMs) to long-term demographic data of the perennial wildflower, Astragalus scaphoides, and explored sensitivity of the results to reduced amounts of data. We compared models of the effect of average temperature, total precipitation, or an integrated measure of drought intensity (standardized precipitation evapotranspiration index, SPEI), on plant vital rates. We found that transitions to flowering and recruitment in year t were highest if winter/spring of year t was wet (positive effect of SPEI). Counterintuitively, if the preceding spring of year t − 1 was wet, flowering probabilities were decreased (negative effect of SPEI). Survival of vegetative plants from t − 1 to t was also negatively affected by wet weather in the spring of year t − 1 and, for large plants, even wet weather in the spring of t − 2 had a negative effect. We assessed the integrated effect of all vital rates on life history performance by fitting FLMs to the asymptotic growth rate, log(). Log() was highest if dry conditions in year t − 1 were followed by wet conditions in the year t. Overall, the positive effects of wet years exceeded their negative effects, suggesting that increasing frequency of drought conditions would reduce population viability of A. scaphoides. The drought signal weakened when reducing the number of monitoring years. Substituting space for time did not recover the weather signal, probably because the weather variables varied little between sites. We detected the SPEI signal when the analysis included data from two sites monitored over 20 yr (2 × 20 observations), but not when analyzing data from four sites monitored over 10 yr (4 × 10 observations).}, - language = {en}, - number = {4}, - urldate = {2024-11-07}, - journal = {Ecology}, - author = {Tenhumberg, Brigitte and Crone, Elizabeth E. and Ramula, Satu and Tyre, Andrew J.}, - year = {2018}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecy.2163}, - keywords = {plant demography, drought, matrix models, carryover effects, environmental drivers, detecting weather signals, space for time substitution}, - pages = {915--925}, - file = {Full Text PDF:/Users/emiliobruna/Zotero/storage/BEEF8XNA/Tenhumberg et al. - 2018 - Time-lagged effects of weather on plant demography drought and Astragalus scaphoides.pdf:application/pdf;Snapshot:/Users/emiliobruna/Zotero/storage/634V9LHS/ecy.html:text/html}, -} - @article{reynoldsDelayedInducedSilica2012, title = {Delayed induced silica defences in grasses and their potential for destabilising herbivore population dynamics}, volume = {170}, @@ -958,5 +1112,25 @@ @book{tuljapurkarPopulationDynamicsVariable1990 editor = {Levin, S.}, year = {1990}, doi = {10.1007/978-3-642-51652-8}, - keywords = {Allele, dynamics, environment, evolution, fertility, growth, population dynamics}, + keywords = {population dynamics, evolution, growth, dynamics, environment, Allele, fertility}, +} + +@article{brunaDemographyUnderstoryHerb2023, + title = {Demography of the understory herb {Heliconia} acuminata ({Heliconiaceae}) in an experimentally fragmented tropical landscape}, + volume = {104}, + copyright = {© 2023 The Ecological Society of America.}, + issn = {1939-9170}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ecy.4174}, + doi = {10.1002/ecy.4174}, + abstract = {Habitat fragmentation remains a major focus of research by ecologists decades after being put forward as a threat to the integrity of ecosystems. While studies have documented myriad biotic changes in fragmented landscapes, including the local extinction of species from fragments, the demographic mechanisms underlying these extinctions are rarely known. However, many of them—especially in lowland tropical forests—are thought to be driven by one of two mechanisms: (1) reduced recruitment in fragments resulting from changes in the diversity or abundance of pollinators and seed dispersers or (2) increased rates of individual mortality in fragments due to dramatically altered abiotic conditions, especially near fragment edges. Unfortunately, there have been few tests of these potential mechanisms due to the paucity of long-term and comprehensive demographic data collected in both forest fragments and continuous forest sites. Here we report 11 years (1998–2009) of demographic data from populations of the Amazonian understory herb Heliconia acuminata (LC Rich.) found at Brazil's Biological Dynamics of Forest Fragments Project (BDFFP). The data set comprises {\textgreater}66,000 plant × year records of 8586 plants, including 3464 seedlings established after the first census. Seven populations were in experimentally isolated fragments (one in each of four 1-ha fragments and one in each of three 10-ha fragments), with the remaining six populations in continuous forest. Each population was in a 50 × 100 m permanent plot, with the distance between plots ranging from 500 m to 60 km. The plants in each plot were censused annually, at which time we recorded, identified, marked, and measured new seedlings, identified any previously marked plants that died, and recorded the size of surviving individuals. Each plot was also surveyed four to five times during the flowering season to identify reproductive plants and record the number of inflorescences each produced. These data have been used to investigate topics ranging from the way fragmentation-related reductions in germination influence population dynamics to statistical methods for analyzing reproductive rates. This breadth of prior use reflects the value of these data to future researchers. In addition to analyses of plant responses to habitat fragmentation, these data can be used to address fundamental questions in plant demography and the evolutionary ecology of tropical plants and to develop and test demographic models and tools. Though we welcome opportunities to collaborate with interested users, there are no restrictions on the use of this data set. However, we do request that those using the data for teaching or research purposes inform us of how they are doing so and cite this paper and the data archive when appropriate. Any publication using the data must also include a BDFFP Technical Series Number in the Acknowledgments. Authors can request this series number upon the acceptance of their article by contacting the BDFFP's Scientific Coordinator or E. M. Bruna.}, + language = {en}, + number = {12}, + urldate = {2024-07-16}, + journal = {Ecology}, + author = {Bruna, Emilio M. and Uriarte, María and Darrigo, Maria Rosa and Rubim, Paulo and Jurinitz, Cristiane F. and Scott, Eric R. and Ferreira da Silva, Osmaildo and Kress, W. John}, + year = {2023}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecy.4174}, + keywords = {Amazon, Brazil, deforestation, demography, edge effects, forest fragments, habitat fragmentation, integral projection models, matrix models, population dynamics, vital rates}, + pages = {e4174}, + file = {Full Text PDF:/Users/emiliobruna/Zotero/storage/P3ML6TL3/Bruna et al. - 2023 - Demography of the understory herb eliconia acumina.pdf:application/pdf;Snapshot:/Users/emiliobruna/Zotero/storage/9NNRVATC/ecy.html:text/html}, }