Joseph L.Ganey,Jose M.Iniguez,Scott C.Vojta and Amy R.Iniguez

Abstract

Keywords:Climate change,Drought,Monitoring,Snag abundance,Snag creation,Snag dynamics,Species composition,Tree mortality

Background

Understanding the effects of climate change on structure and composition of forest ecosystems presents a major challenge for researchers and managers in the twenty-first century(Millar et al.2007).Warming climates already have profoundly affected forests throughout the world(Adams et al.2009;Allen et al.2010),and climate models predict further warming and drying in many areas(e.g.,Seager et al.2007;Stocker et al.2013;Garfin et al.2014)suggesting that changing climates will continue to affect these systems.Forest systems can be affected directly through climate-mediated mortality(Breshears et al.2005;Mueller et al.2005;Adams et al.2009),or indirectly through altered disturbance regimes that result in increased mortality(e.g.,McKenzie et al.2004;Bentz et al.2010;Wan et al.2019)or reduced regeneration(Davis et al.2019).Because drought-mediated mortality may differentially affect tree species and sizes,such mortality may drive significant changes in forest structure and composition(Mueller et al.2005;Ganey and Vojta 2011;Kane et al.2014).

Snags,or standing dead trees,are important components of forest systems that are directly affected by drought-mediated trends in tree mortality.These snags serve as biological legacies in forest systems(Thomas et al.1979;McComb and Lindenmayer 1999;Woldendorp and Keenan 2005),providing habitat for native wildlife(e.g.,Bull et al.1997;Rabe et al.1998),serving as an important source of coarse woody debris(Harmon et al.1986;Laudenslayer et al.2002;Woldendorp and Keenan 2005),and aiding in nutrient cycling and other ecosystem functions.Snags also provide an index of recent tree mortality(Ganey and Vojta 2011;Wu et al.2017),with recruitment of new snags reflecting temporal changes in tree mortality and relative mortality among species and size classes,potentially providing insight into changes in forest structure and composition.

In the southwestern United States,a pronounced increase in snag recruitment(i.e.,tree mortality)followed an extreme climatic year(2002)embedded within a longer-term mega-drought(Breshears et al.2005;Kopeke et al.2010;Williams et al.2020).Several studies reported on short-term trends in tree mortality(which results in snag recruitment)in important forest types following this extreme year(Negron et al.2009[through 2004];Ganey and Vojta 2011[through 2007];Kane et al.2014[through 2008]),but data documenting longerterm trends in these forests are lacking,and few studies focused specifically on snag populations(but see Ganey and Vojta 2005,2012,2014).

Snag populations are governed by the balance between gains and losses in snags.Gains occur when new snags are created by natural tree senescence processes or disturbances such as insect outbreaks,diseases,fire,or droughts.Losses occur when snags are lost to timber or fuelwood harvest,fire,or natural decomposition,processes which are influenced by factors such as snag species and size(Ganey et al.2015).It is therefore desirable not only to document populations of existing snags but also to understand how those populations change over time and the factors responsible for those changes.

Percent change(%)=(2017 value–1997 value)/(1997 value)×100.

Methods

Study area

同时配合公安部下发《城市道路交通信号灯配时智能化和交通标志标线标准化工作指导意见》[3]，即“交通标志标线规范化，信号配时智能化”的要求，通过浮动车数据分析，科学设置杨庄东街交通组织和信号配时，对其进行综合优化和改造[4-10].

再灌注结束后，各组随机取10只动物由腹主动脉取血，室温静置30 min，2 500 r/min离心10 min后取血清，试剂盒法检测CK、LDH活性，操作严格按试剂盒说明书进行，采用比色法于分光光度计660 nm处测定吸光度（A）值，计算CK活力；于440 nm下测定A值，计算LDH活力。

We thank J.Jenness,G.Martinez,M.Stoddard,B.Strohmeyer,and R.White for assisting in establishing plots,and L.Doll,D.and N.Ganey,and C.Vojta for help with plot sampling.J.Ellenwood,B.Higgins,K.Menasco,C.Nelson,and G.Sheppard(Kaibab National Forest)and C.Beyerhelm,A.Brown,H.Green,T.Randall-Parker,C.Taylor,and M.Whitney(Coconino National Forest)assisted with initial plot selection.

Sampling snag and tree populations

In all years,mean snag densities in mixed-conifer forest were approximately five times greater than mean densities in ponderosa pine forest(Fig.1a).Within forest type,mean snag density was similar in 1997 and 2002,increased significantly between 2002 and 2007,and declined slightly after 2007.In mixed-conifer forest,mean snag density remained significantly elevated from 2007 to 2017 relative to earlier years.In contrast,the confidence interval for mean density in 2017 in ponderosa pine forest overlapped with all earlier years,indicating convergence toward pre-2007 levels of snag density from 2012 to 2017.Relative to snag density in 1997,peak mean density in 2007 was 86%and 79%greater in mixed-conifer and ponderosa pine forest,respectively,and mean snag density in 2017 was still 83%and 51%greater than 1997 density.

We marked all snags with numbered metal tags,allowing us to distinguish pre-existing snags from new snags when re-sampling plots(with some exceptions,see below).We recorded species and dbh(nearest cm)for all snags.We sampled plots from May through August,and did not necessarily sample individual plots on the same date or even in the same month across years.Thus,the elapsed time between consecutive samples for an individual plot could range from slightly<5 years to slightly>5 years.We ignored this variability in analysis,and assumed that all intervals between sampling occasions represented a 5-year period.

We sampled live trees≥20 cm in dbh,which were far more abundant than snags,in a 0.09-ha subplot located within each snag plot in 2004 and 2014.We adhered to the minimum 20-cm diameter for consistency with snag sampling.We recorded tree species and dbh(nearest cm)for all trees.We did not mark individual trees,and consequently were not able to determine fates of individual trees.Instead,we focused on overall changes in tree populations,which were driven by the interactions among ingrowth of small(<20 cm dbh in 2004)trees into our sampled population,growth of trees within that sampled population,and tree mortality.As with snags,our inference is limited to trees≥20 cm dbh.

Analysis of snag and tree populations

We included all sampled plots in our analyses of snag and tree populations,including plots subject to recent disturbances such as wild or prescribed fire,for two reasons.First,our objective was to summarize trends in snag and tree populations on the overall landscape,including areas subject to these disturbances.Second,although the effect of fire could be large on individual plots,the overall effect was relatively small over most 5-year intervals between sampling occasions,because relatively few plots were impacted by moderate-to high severity fire during those intervals(Table 1).Consequently,estimates for all plots were similar to estimates including only plots that did not experience moderate-to high-severity fire.We also included plots subject to forest thinning,but these were even fewer in number than burned plots,and thinning activities therefore had minimal immediate impacts on overall snag or tree populations(although they may impact future patterns of tree mortality and/or wildfire).

We treated years in which we sampled snag(or tree)populations as sampling occasions and the intervals between those sampling occasions as“sampling periods”.Thus,for snags we had five sampling occasions(hereafter“years”)and four 5-year sampling periods,whereas for trees we had two years and one 10-year sampling period.Years provided estimates of density and composition for snag(or tree)populations at a point in time,and sampling periods provided estimates of change in those parameters between those points in time.

As noted earlier,our plots covered a very wide range of forest structural conditions.Consequently,snag densities varied widely among plots and were so markedly skewed(Ganey and Vojta 2012)that the utility of standard estimators of central tendency such as the mean or median was limited.Therefore,we used Huber’s Mestimator,a generalized maximum–likelihood estimator that provides robust estimates in distributions containing outliers(Huber and Ronchetti 2009),to estimate central tendency in snag density(by year)and change in snag density(by sampling period).We estimated this parameter and associated 95%bias-corrected confidence intervals using 1,000 bootstrap iterations(Efron andTibshirani 1993)in IBM SPSS Statistics v 23(IBM SPSS Statistics,IBM Corp.,Armonk,NY,2015).All subsequent references to mean values for these and other parameters refer to Huber’s M–estimator.For consistency,we used the same methods for tree populations,although those populations were far less skewed than snag populations.

Table 1 Number(and percent)of plots in northern Arizona mixed-conifer and ponderosa pine forest that experienced moderate-to high-severity fire by 5-year interval between snag sampling occasions.n=53 and 60 plots sampled in mixedconifer and ponderosa pine forest,respectively.Plots were classified as having experienced moderate-to high-severity fire if a visual inspection indicated that considerable tree mortality had occurred within the plot due to fire between sampling occasions

The skewed distributions for snag densities also limited the utility of standard hypothesis tests.Consequently,we used the bootstrapped confidence intervals discussed above to assess significance of observed differences in snag density.For comparisons between years or sampling periods,we assumed that confidence intervals that did not overlap between pairs of years or sampling periods indicated that those years or sampling periods differed significantly from each other.For change during sampling periods,we assumed that confidence intervals that did not overlap zero indicated that snag(or tree)density changed significantly during that period.

Changes in snag and tree density

We estimated snag and tree density within each plot for each year,then summarized snag and tree density across plots within forest type for all years.Because snags were uniquely marked,we also were able to estimate numbers of new snags recruited and existing snags lost during each sampling period.Numbers of new snags may be slightly overestimated for some plots and sampling periods,because the metal tags used to mark snags sometimes melted in plots that experienced moderate-to high-severity fire,making it difficult to distinguish new snags recruited post-fire from snags present before the fire.We suspect that this bias was small,however,for two reasons.First,these disturbances affected relatively few plots during most sampling periods(Table 1).Second,fires hot enough to melt the metal tags also burned most existing snags,meaning that most apparent“new”snags in these areas likely were new.Detection rates for standing snags in unburned plots,estimated using markresight methodology,were very high,ranging from 0.983 to 0.994 across major snag species represented(Ganey et al.2015:Table 3).

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For each sampling period,we estimated numbers of snags gained and lost by plot,then summarized these parameters across plots within forest type.Because numbers of snags available at the start of a sampling period varied,we also estimated standardized snag loss as:

Percentage of snag loss(%)=(snags lost from time t to t+5 years)/(snag density at time t)×100.

Composition of snag and tree populations

We compared species composition and diameter-class distributions of snag and tree populations among years within forest type using chi-square tests(Conover 1980).We pooled snags or trees across plots within forest type for these comparisons.

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We included six major species groups in comparisons of species composition in mixed-conifer forest(white fir,Douglas-fir,quaking aspen,ponderosa pine,Gambel oak,and a sixth group[Other]representing all other species).We included only three species groups(ponderosa pine,Gambel oak,and Other)in tests in ponderosa pine forest.Other species were present in such small amounts in this forest type that including those species as separate categories resulted in multiple cells with expected values<5,potentially biasing test results(Conover 1980).In contrast,no cells had expected values<5 after collapsing categories.We recognized five diameter classes in tests involving diameter-class distributions:20–29,30–39,40–49,50–59,and ≥60 cm dbh.We estimated percent change in individual snag species or diameter classes during the 20-year study period as:

Since 1997,we have sampled snag and tree populations periodically in mixed-conifer and ponderosa pine forests in northern Arizona.Previous papers from this study documented changes in snag populations over 5-,10-,and 15-year increments within this 20-year period(Ganey and Vojta 2005,2012,2014)as well as patterns in:tree mortality from 2002 to 2007(Ganey and Vojta 2011)and snag longevity from 1997 to 2015(Ganey et al.2015).Here,we expanded on this earlier work to summarize drought-mediated changes in snag and tree abundance,and composition of snag and tree populations within these forest types,over the 20-year period from 1997 to 2017,with an emphasis on temporal changes in the snag population,including rates of snag recruitment and loss and the factors driving these processes.Our specific objectives included:(1)Evaluating temporal trends in snag and tree density and snag recruitment and loss rates,(2)Summarizing climate during the study period and evaluating potential relationships between climate patterns and snag recruitment,and(3)Evaluating temporal trends in composition and structure of overall snag populations,as well as in snags that were recruited or lost during the intervals between sampling occasions.These data thus provide information on trends in snag populations during the study period as well as on the factors driving those trends.This information should aid forest managers in understanding the potential effects of future climate patterns on these snag populations,and,to a lesser extent,on the tree populations from which they derive.

Climate data

Because climate can strongly affect tree mortality and thus snag creation,we obtained and summarized data on annual precipitation(AP)and cooling degree days(CDD)for the National Weather Service(NWS)weather stati on at Pul l iam ai rport in Fl agstaff,AZ(http://w2.weather.gov/climate/xmacis.php?wfo=fgz; downloaded 14 Dec 2017).We assumed that this station,which was centrally located within the study area at an elevation of 2136 m,provided a valid index to annual variation in broad-scale climate patterns across the study area.We restricted our analysis to the period from 1950 to 2016 because data on CDD were not available for most years prior to 1950.We assumed that this 67-year period,which included the well documented mid-20th century drought prominent throughout the southwest(Hereford 2007),provided a valid index to broad-scale climate patterns across the study area in recent times.

CDD were calculated by NWS using a base temperature of 65°F.Thus,CDD was calculated only for days when the mean temperature was greater than 65°F as:

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