Spatiotemporal patterns of diversity and diversity-stability relationships as a function of compounding disturbances and forest management

Abstract

Context
Consistent with the diversity-stability hypothesis, high wildlife diversity has been associated with increased resilience and stability of ecosystem services and functions. Nevertheless, ecological non-stationarity associated with climate change challenges the concept of stability. Furthermore, ambiguity surrounding appropriate diversity metrics to use has hindered the ability of natural resource managers to leverage the potential benefits of biodiversity conservation.

Objectives and methods
We aimed to infer how diversity and compositional stability might be affected by multiple climate and disturbance stressors, including management activity.

Methods
We used a spatially explicit landscape succession model to predict spatiotemporal patterns of beta diversity for terrestrial vertebrates representing three trophic groups (herbivores, insectivores, and predators) over an 80-year time span.

Results
Trends in diversity were driven by species gains at higher elevations and species losses at lower elevations, however, species reorganization was modified by both mean species turnover (i.e. replacement of species across space) as well as management intensity. Higher species turnover was associated with greater among-site compositional stability and decreased local compositional change attributed to species losses for all trophic groups. Increasing management intensity further increased beta diversity across all elevations whereas decreasing management intensity led to spatial homogenization of herbivores and insectivores at low elevations. High management intensity also weakened naturally occurring diversity-stability relationships at larger spatial scales.

Conclusions
Increasing management intensity may be beneficial at lower elevations where projections anticipate species losses and homogenization. Additionally, conserving areas of high diversity will likely be important for promoting future compositional stability for trophic groups that support key ecological processes.

Full text

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Landsc Ecol (2025) 40:84
https://doi.org/10.1007/s10980-025-02075-3
RESEARCH ARTICLE
Spatiotemporal patterns ofdiversity anddiversity‑stability
relationships asafunction ofcompounding disturbances
andforest management
KiraL.Hefty · NicholasA.Povak · PatriciaN.Manley ·
SamuelW.Flake · KatherineA.Zeller
Received: 8 February 2024 / Accepted: 26 February 2025
This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2025
has hindered the ability of natural resource manag-
ers to leverage the potential benefits of biodiversity
conservation.
Objectives and methods We aimed to infer how
diversity and compositional stability might be
affected by multiple climate and disturbance stress-
ors, including management activity.
Methods We used a spatially explicit landscape suc-
cession model to predict spatiotemporal patterns of
beta diversity for terrestrial vertebrates representing
three trophic groups (herbivores, insectivores, and
predators) over an 80-year time span.
Results Trends in diversity were driven by spe-
cies gains at higher elevations and species losses at
lower elevations, however, species reorganization was
modified by both mean species turnover (i.e. replace-
ment of species across space) as well as manage-
ment intensity. Higher species turnover was associ-
ated with greater among-site compositional stability
and decreased local compositional change attributed
to species losses for all trophic groups. Increasing
management intensity further increased beta diversity
across all elevations whereas decreasing management
intensity led to spatial homogenization of herbivores
and insectivores at low elevations. High management
intensity also weakened naturally occurring diversity-
stability relationships at larger spatial scales.
Conclusions Increasing management intensity may
be beneficial at lower elevations where projections
anticipate species losses and homogenization. Addi-
tionally, conserving areas of high diversity will likely
Abstract
Context Consistent with the diversity-stability
hypothesis, high wildlife diversity has been associ-
ated with increased resilience and stability of ecosys-
tem services and functions. Nevertheless, ecological
non-stationarity associated with climate change chal-
lenges the concept of stability. Furthermore, ambigu-
ity surrounding appropriate diversity metrics to use
Supplementary Information The online version
contains supplementary material available at https:// doi.
org/ 10. 1007/ s10980- 025- 02075-3.
K.L.Hefty(*)· K.A.Zeller
Aldo Leopold Wilderness Research Institute, Rocky
Mountain Research Station, USDA Forest Service, 790 E
Beckwith Ave, Missoula, MT, USA
e-mail: Kira.Hefty@usda.gov
K. A. Zeller
e-mail: Katherine.Zeller@usda.gov
N.A.Povak
Pacific Southwest Research Station, USDA Forest Service,
626 E. Wisconsin Ave, Milwaukee, WI53202, USA
e-mail: Nicholas.Povak@usda.gov
P.N.Manley
Pacific Southwest Research Station, USDA Forest Service,
2480 Carson Road, Placerville, CA95667, USA
e-mail: Patricia.Manley@usda.gov
S.W.Flake
Department ofForestry andEnvironmental Resources,
North Carolina State University, Raleigh, NC27695, USA
e-mail: swflake@ncsu.edu
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be important for promoting future compositional sta-
bility for trophic groups that support key ecological
processes.
Keywords Diversity-stability hypothesis·
Beta diversity· Climate change· Biodiversity·
Management
Introduction
Biodiversity can have profound impacts on ecosystem
stability through time and space. According to the
diversity-stability hypothesis, ecological communi-
ties with higher species diversity are associated with
greater persistence of important ecosystem functions
and services, including carbon sequestration, produc-
tivity, and nutrient cycling (Tilman etal. 1996; Alt-
ieri 1999; Ruifrok etal. 2015; Yang etal. 2019). As
such, biodiversity conservation has recently become
a priority for sustainable management practices (Kuu-
luvainen et al. 2021; Oettel & Lapin 2021). Never-
theless, synergistic effects of climate change with
other human-driven and natural disturbances are
increasingly altering species composition on local-
to landscape-level scales (Oliver & Morecroft 2014;
Kelly etal. 2020). Species losses or wildlife commu-
nity reorganization can lead to irreversible changes
to ecosystem patterns and processes that may either
improve or detract from resilience to future envi-
ronmental perturbations and impact the services
that these systems provide (Cardinale et al. 2012).
Whether the diversity-stability hypothesis still holds
true given increased intensity and frequency of com-
pounding disturbance events, including management
activity, remains largely untested.
Biodiversity is known to influence stability in
ecological communities in several ways, including,
1) through complementarity where niche differen-
tiation facilitates the persistence of multiple spe-
cies whose collective participation support a spe-
cific ecosystem function (Loreau & Hector 2001),
2) through redundancy in which species are inter-
changeable in their contribution to a specific eco-
system function (McCann 2000; Biggs etal. 2020),
and 3) through species asynchrony in which species
differ in their responses to acute or prolonged envi-
ronmental changes thereby decreasing the likeli-
hood of community collapse and subsequent loss of
ecosystem functioning (Tilman 1996; de Mazancourt
etal. 2013; Sasaki etal. 2019). When all these fac-
tors are present, overall ecosystem function can likely
withstand modest local species losses associated with
sudden or gradual environmental change (Yachi &
Loreau 1999). In contrast, landscapes characterized
by low diversity are more likely to experience nega-
tive impacts to services with species losses and are
therefore suspected to be less robust to environmental
change (Oliver etal. 2015; Sasaki etal. 2019).
These interpretations assume that ecological com-
munities exist within an equilibrium where resilient
communities can return to and maintain a stable
ecosystem state following disturbance, also known
as ecological stationarity (Tilman 1996). Climate
change threatens the idea of ecological stationar-
ity because disturbances work synergistically with
changing climatic conditions to push ecological
communities outside their historical range of varia-
tion (Milly etal. 2008). Species additions or replace-
ments in transforming ecosystems can drastically
alter existing interactions and patterns, leading to
new ecosystem states and potentially altering overall
ecosystem function (Pecl etal. 2017). Whether diver-
sity can promote stability in communities experienc-
ing increased frequency and intensity of disturbance
associated with climate change and human activity
moves beyond the traditional evaluation of the diver-
sity-stability hypothesis and requires further investi-
gation (Tilman 1996; Sankaran & McNaughton 1999;
García-Palacios etal. 2018).
Ecological stability has been characterized in
many ways (Grimm & Wissel 1997). Stability or,
inversely, variability in communities may be attrib-
uted to spatial and temporal patterns of aggregate
properties, such as biomass, productivity, and abun-
dance, or compositional similarities or dissimilari-
ties among species assemblages (Cottingham et al.
2001; Lamy etal. 2021). Recent studies have focused
on assessing compositional stability because of its
ability to explain underlying mechanisms driving
changes in ecosystem properties, such as biomass and
species abundance (Mellin et al. 2014; Hillebrand
etal. 2017a; Wisnoski etal. 2023). As such, under-
standing how species assemblages may reorganize
in response to environmental change is an essential
step in understanding how ecosystem function may
be altered in transforming systems. Alpha, beta, and
gamma diversity metrics have been used to quantify
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compositional variability, however, the contribu-
tion and interpretation of each metric for stability is
dependent on the spatial scale at which these met-
rics are assessed (Mellin etal. 2014; Cardinale etal.
2018). While alpha diversity may be a good predic-
tor of local-level ecological stability, it may not per-
form as well as other metrics (e.g. beta diversity) in
predicting stability in ecological functions over larger
spatial scales (Hillebrand etal. 2017a, b; Catano etal.
2020). For example, in landscapes altered by climate
change, environmental filtering may lead to biotic
homogenization that is not detectable on a local-
scale. Beta diversity links local-scale species richness
with landscape-level patterns of diversity to provide
more detailed information on biodiversity change and
subsequent impacts to ecosystem function and stabil-
ity (Cazalis 2022). Furthermore, the two components
of beta diversity–turnover and nestedness – can be
used to measure trends in species composition across
larger spatial scales, including homogenization, and
attribute those trends to underlying processes (Soco-
lar etal. 2016). Nestedness accounts for dissimilarity
in species composition associated with species gains
and losses among sites, such that individual units may
be subsets of other more species rich units (Baselga
2010). In contrast, species turnover measured the
replacement of some species by others between sites,
independent of differences in species richness. Spe-
cies turnover, in particular, can elucidate how com-
munities may be reorganizing along topographical
gradients or in response to acute or prolonged distur-
bance (Jankowski etal. 2009).
In forested systems in North America, individual
natural disturbances such as wildfire, beetle damage,
and human-caused disruptions such as fragmentation
and climate change may result in sudden or delayed
changes in species composition and ecosystem struc-
ture (Malhi et al. 2020). To confront these changes
and improve ecosystem resilience to future distur-
bance, management activities such as forest thinning
and fuel reduction are often used to alter forest struc-
ture to reduce the spread and severity of disturbance
events. Nevertheless, compounding environmental
stressors can alter ecological communities in ways
that are difficult for managers to predict, prepare for,
and respond to (Chmura etal. 2011; Stephens etal.
2014; Moriarty et al. 2016; Northrup et al. 2019).
Furthermore, natural resource management has not
traditionally targeted biodiversity conservation as an
intervention goal, thus there is limited empirical evi-
dence of how varying levels of management intensity
may affect spatiotemporal patterns in species assem-
blages in a climate change-driven system. Using beta
diversity metrics to monitor compositional variability
can help elucidate how ecosystem function and resil-
ience may be changing in response to multiple stress-
ors, including management activity (Dell etal. 2019;
Jones et al. 2021). Concurrently, uncovering how
management activities may or may not buffer ecologi-
cal communities from species gains, losses, and com-
positional homogenization can help managers better
decide when, where, and how much—if any—man-
agement is needed to support stability in transforming
landscapes.
To understand how synergistic effects of diversity,
compounding disturbances, and forest management
may influence emerging patterns of diversity and
stability in ecological communities, we used a spa-
tially explicit landscape succession model to quantify
spatiotemporal changes in terrestrial wildlife species
composition of three prominent trophic groups (her-
bivores, insectivores, and predators) over 80 years.
We first analyzed the influence of spatial complex-
ity on patterns of diversity alone, then incorporated
regional patterns of beta diversity and forest manage-
ment scenarios to assess the following questions: 1)
How do patterns of beta diversity change over space
and time under climate change?, 2) Does high spatial
beta diversity reduce temporal compositional vari-
ability in a system affected by climate change?, and 3)
Can forest management alter spatiotemporal patterns
of beta diversity and beta diversity-stability relation-
ships in a changing climate? Information gathered
from our analyses were used to inform science-based
decision-making efforts in the central Sierra Nevada
Mountains of California, USA.
Methods
Study area
Our study area encompassed a 970,000-ha forested
landscape in the central Sierra Nevada mountains
of California and Nevada, USA (Fig.1). This land-
scape was defined as part of the Tahoe Central Sierra
Initiative Planning project with a goal to implement
planning and research efforts that promote ecosystem
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resilience (Manley etal. 2023). Elevation of the study
area ranges from 900 to 3300m and includes forested
terrestrial vegetation communities from oak wood-
lands on low-elevation, west-facing slopes to mixed
conifer and alpine at mid- to high elevations. At lower
elevations, vegetation communities are rain-driven,
while at high elevations communities are snow-
driven. The study area encompasses private and pub-
lic lands, including wilderness areas, state-managed
land, other federally managed land, rural communi-
ties, and small cities.
Dynamic modeling: LANDIS‑II, climate change,
andforest management
We used the LANDIS-II forest vegetation frame-
work to simulate effects of climate change, forest
management, beetle damage, and wildfire on vegeta-
tion (Scheller etal. 2007). LANDIS-II is a spatially
explicit landscape simulation model that has been
used to model forest growth and succession dynam-
ics in forested ecosystems across the globe. Within
LANDIS-II, we implemented the Net Ecosystem
Carbon and Nitrogen extension (Scheller etal. 2011),
which models forest establishment and growth and
provides a total ecosystem accounting of carbon and
nitrogen. Vegetation dynamics are responsive to cli-
mate, as well as a range of insitu disturbances such
as insect outbreaks and fire. We modeled vegetation
and disturbance dynamics for 80years at 180-m reso-
lution, and derived forest structure and compositional
characteristics from biomass within age class and tree
or shrub species cohorts. We chose to forecast forest
composition and structural change using a less con-
servative climate modeling approach, which we felt
best represented current trends in emissions. The
MIROC-ESM (Model for Interdisciplinary Research
on Climate-Earth System Model; Watanabe et al.
2011) predicts a warmer, drier future for central
California coupled with representative concentration
pathway 8.5 (RCP 8.5), which best represents a “busi-
ness as usual” emissions future.
We assigned habitat suitability for faunal species
for each raster cell at each time step to a habitat type
based on the California Wildlife Habitat Relationship
(CWHR) system (California Department of Fish and
Wildlife 2014). We estimated habitat types by cross
walking cell-level biomass outputs to relevant stand
metrics that described forest type, canopy cover,
quadratic mean diameter (QMD), and seral stage
(below). Each 180-m cell was classified into one of 14
vegetation types depending on the dominant species
present at that timestep (Zeller etal. 2023). We devel-
oped regression models from Forest Inventory and
Analysis data (FIA; https:// www. fia. fs. fed. us) to esti-
mate diameter from cohort ages, which we then used
to calculate the weighted mean diameter (weighted
by cohort biomass) for each plot. The weighted mean
diameter was then binned into seral stages (< 1cm,
1–6 cm, 6–11 cm, 11–24 cm, > 24 cm). We also
developed regression equations to estimate percent
canopy cover from vegetation type, seral stage, and
plot biomass. Canopy cover was estimated for each
raster cell and then assigned to one of five cover
classes: < 10%, 10–25%, 25–40%, 40–60%, > 60%.
From the categorical maps of vegetation type, seral
stage, and canopy cover class, we used the CWHR
database to estimate habitat suitability for terrestrial
vertebrate species following the methods outlined by
White etal. (2022).
We selected three LANDIS-II management
scenarios from six total scenarios used in a larger
modeling effort within the study area (Maxwell
etal. 2022). The three management scenarios were
selected to represent a range of treatment intensi-
ties that varied by the type of management applied,
its frequency, and spatial footprint. In a low inten-
sity management scenario, mechanical forest
Fig. 1 Map depicting the Tahoe Central Sierra Initiative study
area in central California and Nevada, United States
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thinning occurred only within the wildland-urban
interface (402 m buffer from human development
and private timberlands); in a moderate intensity
management scenario, landscape-wide mechani-
cal forest thinning and prescribed fire management
mimicked a 30–50 year return interval; and in a
high intensity management scenario, landscape-
wide mechanical forest thinning and prescribed
fire management mimicked the historical natural
fire cycle (20–40 year return interval). The low
intensity scenario treated 23,000 ac · yr−1, moder-
ate treated 81,000 ac · yr−1, and the high scenario
treated 127,000 ac · yr−1. Both the moderate and
high intensity scenarios included treatments within
and beyond the wildland-urban interface, including
roadless and wilderness management areas.
Wildlife habitat modeling
The CWHR system uses scientific records and expert
input to estimate wildlife species habitat use based
on discretely defined vegetation communities. We
selected native, non-migratory terrestrial wildlife spe-
cies for which breeding habitat occurred within the
bounds of the TCSI. We classified habitat as either
non-habitat, cover, feeding habitat, or breeding habi-
tat, respectively on a scale from 0 to 0.33. We chose
to focus on breeding habitat as the best approxima-
tion of species occurrence and therefore filtered the
CWHR output to reflect this decision. To do this, we
converted the CWHR scale to binary by converting
any value less than 0.3 (least likely breeding habi-
tat) to zero and anything greater than or equal to 0.3
(most likely breeding habitat) to one. We used these
new binary values to approximate potential presence
and absence for 203 species (Appendix A). For all
further purposes of this paper, this approximation is
used to describe patterns of diversity and diversity-
stability relationships.
To better understand how ecosystem function
may be affected by changes in patterns of diversity
over time, we grouped species into three key trophic
groups: herbivores, insectivores, and predators. In
total, we included 97 herbivore species, 103 insec-
tivores, and 58 predators. Species were sorted into
groups based on their dominant feeding habits. If spe-
cies were strong omnivores (n = 50), they were placed
in multiple groups (ex: black bears, birds, raccoons).
Spatial and temporal beta diversity indices
We used the Sørensen family of compositional dis-
similarity indices to calculate both spatial and tempo-
ral beta diversity to track changes in terrestrial wild-
life species composition (Table 1). All calculations
were completed in Program R (R Core Team 2022).
We incorporated functions from the R package beta-
part to calculate spatial dissimilarity within a 1-km2
moving window (Baselga & Orme 2012). The 1-km2
moving window allowed us to detect compositional
change for species with relatively small home ranges
as well as species with intermediate to large ranges
(Melo etal. 2009). We calculated dissimilarity as the
mean of pairwise differences between a focal cell and
all its neighbors within each 1-km2 neighborhood.
We further partitioned our spatial dissimilarity results
using the Baselga framework (Baselga 2010), which
allowed us to isolate the turnover component of beta
diversity (i.e. species replacement across space: βsim).
βsim is independent of richness differences and there-
fore represents true species replacement across space
(Baselga & Leprieur 2015). βsim was scaled from 0
to 1, with 0 indicating identical species composition
among 180-m2 cells within the 1-km2 neighborhoods
and 1 indicating complete dissimilarity in composi-
tion among constituent raster cells. We qualitatively
assessed trends in βsim among trophic groups through
time and quantitatively assessed the contribution of
mean current βsim (i.e., initial spatial species turnover
from 2020) to emerging trends in other diversity met-
rics and for subsequent diversity-stability relation-
ships as described below.
We calculated local temporal variability in species
composition (i.e. temporal beta-diversity index: TBI;
Legendre 2019) between current and future time steps
for each 180-m2 raster cell. We used the current time
step, 2020, as a reference to measure dissimilarity to
each subsequent time step (e.g.: 2020 to 2030, 2020
to 2040, etc.). Because species composition change
is directional in temporal comparisons, we parti-
tioned TBI into percent of dissimilarity attributed to
either species gains or losses for each pairwise time
step (Legendre 2019). We interpreted species losses
as species leaving a locality to track suitable climatic
and vegetation conditions elsewhere through time
and we interpreted species gains as species arriving
in new localities where emerging climatic and veg-
etation conditions became suitable within the cell.
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Table 1 Description of beta diversity indices used in a spatiotemporal analysis of terrestrial wildlife diversity in the central Sierra Nevada Mountains, USA, from 2020 to 2100
Metric Description Interpretation Shorthand Statistical Test
Regional species turnover Among-site spatial measure of species dis-
similarity, partitioned from the spatial beta
diversity calculation (moving window calcula-
tion across study area)
Increasing values indicate increasing spatial
diversity in species composition
βsim Predictor, GLM and
LM with BoxCox
transformation
Temporal compositional variability Local-level measure of temporal beta diversity
among cells through time
Higher values indicate greater dissimilarity in
local communities through time
TBI Response, GLM
Temporal species loss Measure of species losses partitioned from the
temporal beta diversity calculation (single cell
comparison across time)
Higher values indicate greater percent of total
temporal beta diversity attributed to species
losses at the local-scale
TBIloss Response, GLM
Percent change in regional species turnover Mean percent change in species turnover
partitioned from the spatial beta diversity
calculation (moving window calculation) from
2020–2100
Higher values indicate increasing spatial diver-
sity in species composition. Negative values
indicate increasing homogenization
%Δ βsim Response, Cohen’s D
Among-site compositional stability Inverse coefficient of variation for spatial spe-
cies turnover from 2020–2100 partitioned
from the spatial beta diversity calculation
(moving window calculation)
Higher values indicate greater stability in spe-
cies composition over time
βsimICV Response, LM with
BoxCox transfor-
mation
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We interpreted trends in species losses in space and
time as an indicator of changing species composition.
We scaled the index of temporal species loss (TBIloss)
between 0 and 1, with 0 indicating no detectable tem-
poral change in species composition attributable to
species losses between T1 and T2 and 1 indicating
that 100% of temporal change in species composition
was attributable to species losses.
To reduce computational burden and achieve
meaningful statistical results, we took a random sam-
ple of points across the TCSI study area. To deter-
mine an adequate number of sample points, we pro-
gressively increased the number of potential random
points by increments of 500 until the sample propor-
tionally represented the distribution of underlying
topographic and diversity metrics within the study
area. Because landscape conditions changed between
simulated years, the extent of usable wildlife breed-
ing habitat within our study area also changed slightly
between years. To address this, we additionally elimi-
nated random points that fell in cells that contained
no data for any year. We extracted all diversity met-
rics to these points for further analysis for each of the
three following research topics. Our methods resulted
in a total of 4,900 samples.
Q1: Diversity over space and time in the context of
climate change
To address our first question, how patterns of diver-
sity change across space and through time in a chang-
ing climate, we used random forest and regression
trees to assess relationships between our spatial and
temporal beta diversity metrics and topographic fea-
tures such as elevation, slope, terrain ruggedness,
and aspect. Initial visual observation of patterns and
trends in beta diversity metrics suggested a strong
influence of elevation. We modeled all topographic
features at a 30-m resolution and extracted values to
our 4900 random sample points. We evaluated ran-
dom forest models using a tenfold cross validation
technique trained on 80% of the original dataset. We
used a grid search method to identify optimal hyper-
parameters that minimized model root mean squared
error (RMSE), and the final model was validated
using the testing set. We assessed predictor vari-
able importance based on their percent contribution
to mean square error. Of the topographic variables
assessed, elevation was consistently associated with
the highest mean percent increase in mean square
error in our random forest models (62.76%Inc-
MSE ± 51.95 SD). We further examined how our data
may be partitioned along splits of elevation by exam-
ining single regression trees for each model. We used
the root splits for elevation to inform further analyses.
Q2: Compositional variability as a function of
species turnover and climate change
To examine our second question–does high spatial
diversity reduce temporal compositional variability
in a system affected by climate change–we meas-
ured compositional variability using three statistical
approaches and evaluations were made at two spatial
scales. At the local scale (180-m2 cells), we assessed
compositional variability as mean temporal variabil-
ity in species composition (TBI) and mean percent
of temporal change attributed to local species losses
(TBIloss). We interpreted high values of mean TBI (at
or approaching 1) as being highly variable through
time, whereas low values (at or approaching 0) were
interpreted as having low variability through time. We
interpreted high values of TBIloss as high likelihood
that compositional change was associated with spe-
cies losses and low values as low likelihood that com-
positional change was associated with species losses.
At the neighborhood scale (1-km2) of our spatial beta
diversity metrics, we assessed spatial compositional
variability by measuring stability in spatial species
turnover (herein, among-site stability or βsimICV) over
time. We calculated βsimICV as the inverse temporal
coefficient of variation of βsim for future years 2030
to 2100 (Wagg etal. 2022). High values of βsimICV
indicated high stability in among-site species com-
position over time whereas low values indicated low
stability.
Finally, we used linear and generalized linear mod-
eling approaches to evaluate the potential role of ele-
vation and mean species turnover (βsim) for all future
timesteps (2030–2100) on explaining observed pat-
terns and trends in the 1) local temporal variability of
species turnover (TBI), 2) local compositional change
attributed to species losses (TBIloss) over time and, 2)
among-site stability (βsimICV) for each trophic group.
We used a generalized linear model with binomial
error distribution and logit link function to assess
relationships between mean βsim and mean TBI and
TBIloss (Ferrier et al. 2007). To assess relationships
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between mean current βsim (2020) and mean future
trends in βsimICV, we used linear regression modeling
and applied the Box-Cox transformation technique
to normalize our dependent data (Box & Cox 1964).
Elevation was scaled using the z-score prior to all
analyses.
Q3: Management impacts to spatiotemporal patterns
of diversity and diversity-stability relationships
To address our third question–can management inter-
vention alter spatiotemporal patterns of diversity and
compositional diversity-stability relationships in a
changing climate–we used the modeling approaches
described above and included management intensity
as a predictor of trends in TBI, TBIloss, and βsimICV.
Additionally, we assessed the influence of manage-
ment intensity on percent change in spatial species
turnover (%Δ βsim) from 2020 to 2100. We inter-
preted negative values for %Δ βsim as trending toward
homogenization whereas positive values indicated
increasing dissimilarity overtime. We chose to use
mean spatial species turnover (βsim) to represent
homogenization because it is a true measure of spe-
cies replacement, whereas total spatial beta diversity
is not independent of richness differences.
For all analyses, we treated management scenario
as a factor with three levels (low, moderate, and high).
We used Cohen’s D effect size calculations to detect
potential differences among management scenarios
for %Δ βsim. We assessed how management interven-
tion may be impacting diversity-stability relationships
by including management intensity as an interacting
explanatory variable with βsim in our models predict-
ing TBI, TBIloss, and βsimICV.
Results
Q1: Diversity over space and time in the context of
climate change
Elevation was depicted as the root node in each
regression tree analysis with splits along an eleva-
tional range of 1271m to 2107m. The average root
node split for elevation was 1738.89m ± 295.95 SD.
1700m is near the current persistent snowline in the
central Sierra Nevada Mountains (Shulgina et al.
2023) and, therefore, this value was used to split
our dataset into two categories for further analyses
where elevation wasn’t specifically targeted as a pre-
dictor: low elevation (< 1700m) and high elevation
(≥ 1700m). This two-way split in elevation was par-
ticularly evident for static patterns of βsim as well as
trends in temporal dissimilarity (TBI) among trophic
groups.
The three trophic groups displayed slightly dif-
ferent patterns in mean spatial species turnover
(βsim) in 2020 and 2100 (Fig. 2). At lower eleva-
tions, insectivores maintained the highest over-
all mean βsim for future time steps 2030–2100
(0.094 ± 0.049 SD), whereas predators had the low-
est mean βsim (0.046 ± 0.028 SD). At higher eleva-
tions, herbivores replaced insectivores as having
Fig. 2 Among-site spatial
species turnover (βsim) for
three trophic groups (herbi-
vores, insectivores, preda-
tors) between two timesteps
(2020 and 2100) and for
three forest management
intensity scenarios (low,
moderate, high). Study area
encompasses 1-M ha of
the central Sierra Nevada
Mountains, USA
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the highest overall mean future βsim (0.116 ± 0.040
SD) and predators again had the lowest mean βsim
(0.052 ± 0.021 SD). There was no notable differ-
ence in mean βsim for herbivores in 2020 between
high and low elevations (< 1700m: 0.106 ± 0.059
SD; ≥ 1700 m: 0.104 ± 0.063 SD), however, by
2100 mean βsim had decreased at lower elevations
and increased at higher elevations (< 1700 m:
0.067 ± 0.058 SD; ≥ 1700m: 0.106 ± 0.043 SD). For
insectivores, mean βsim was higher at low elevations
in 2020 (< 1700 m: 0.125 ± 0.065 SD; ≥ 1700 m:
0.064 ± 0.046 SD), but by 2100 this difference
became less apparent as mean βsim decreased at
lower elevations and increased slightly at higher
elevations (< 1700 m: 0.077 ± 0.056 SD; ≥ 1700 m:
0.078 ± 0.039 SD). Similar to herbivores, there
was little difference in mean βsim between higher
and lower elevations for predators in 2020. Unlike
herbivores and insectivores, mean βsim for preda-
tors remained similar between 2020 and 2100 at
higher elevations (2020 ≥ 1700 m: 0.050 ± 0.040
SD; 2100 ≥ 1700 m: 0.050 ± 0.024 SD), however,
mean βsim decreased slightly at lower elevations
(2020 < 1700m: 0.054 ± 0.039 SD; 2100 < 1700 m:
0.046 ± 0.031 SD).
Drivers of and trends in mean local temporal com-
positional variability (TBI) were consistent among all
trophic groups. At elevations below 1700m, TBI was
driven by species losses whereas species gains drove
TBI above 1700m (Fig.3). Additionally, the trend in
TBI increased logarithmically over time for all trophic
groups and management scenarios. Insectivores had
the highest mean TBI over 80 years from 2020 to
2100 for both high and low elevations (< 1700 m:
0.154 ± 0.114 SD; ≥ 1700 m: 0.223 ± 0.152 SD).
Predators displayed the lowest mean TBI from 2020
to 2100 at lower elevations (< 1700m: 0.097 ± 0.066
SD), whereas herbivores displayed the lowest mean
TBI at higher elevations (≥ 1700 m: 0.203 ± 0.113
SD). TBI was higher overall at higher elevations as
Fig. 3 Trend in mean local
temporal compositional
variability of herbivores
expressed by total temporal
beta diversity (TBI) and
its components of species
gains (gray) and losses
(red). Dissimilarity was
assessed among sequential
lag years between 2020 and
2100 and at low (< 1700m)
and high (≥ 1700m) eleva-
tion areas in the central
Sierra Nevada Mountains,
USA
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compared to lower elevations across an 80-year time
span for all trophic groups.
Q2: Compositional variability as a function of
species turnover and climate change
Although local variability in temporal species compo-
sition (TBI) trended higher with increasing elevation,
among-site compositional stability (βsimICV) displayed
varied trends dependent on trophic group, indicating
that individual cell-based changes in species losses
were not always driving change in regional patterns
of diversity over time (Fig. 4). TBI was positively
associated with mean current (2020) among-site spa-
tial turnover (βsim) for all trophic groups (herbivores
coefficient: 4.75, SE: 0.66, p < 0.001; insectivores
coefficient 4.78, SE: 0.68, p < 0.001; predators coef-
ficient: 6.90; SE: 1.10, p-value < 0.001). For insecti-
vores and predators, the strength of the relationship
between βsim and TBI decreased with every unit
increase in elevation (insectivores coefficient: -2.89,
SE: 0.71, p < 0.001; predators coefficient: -3.23,
SE: 1.12, p = 0.004). All trophic groups displayed
increasing βsimICV with increasing current βsim inde-
pendent of elevation. However, for herbivores, with
each unit increase in elevation, the effect of βsim on
βsimICV decreased, indicating that the influence of βsim
on βsimICV decreased at high elevations (coefficient:
-1.35, SE: 0.12, p < 0.001). There was also a posi-
tive relationship between βsim and βsimICV for preda-
tors, however, unlike herbivores and insectivores,
the relationship between elevation and βsimICV was
negative, indicating lower stability at high elevations
(coefficient: -0.10, SE: 0.01, p < 0.001). Additionally,
there was no significant interaction between βsim and
elevation for predicting βsimICV for insectivores or
predators.
Elevation was negatively associated with mean
TBIloss for all trophic groups, indicating that species
losses were more apparent at lower versus higher
elevations. For all trophic groups, mean current βsim
(2020) was negatively associated with mean TBIloss
(herbivores coefficient: -3.29, SE: 0.51, p < 0.001;
insectivores coefficient: -2.33, SE: 0.55, p < 0.001;
predators coefficient: –15.31, SE: 0.91, p < 0.001).
Unlike the relationship with βsimICV, the effect of
βsim on TBIloss increased with every unit increase in
elevation for herbivores, indicating that the influence
of βsim on TBIloss was greater at high elevations (her-
bivores coefficient: 4.05, SE: 0.60, p < 0.001). There
was no significant interaction with elevation for either
insectivores or predators, indicating consistency in
the relationship between βsim and TBIloss independent
of elevation.
Q3: Management impacts to spatiotemporal patterns
of diversity and diversity-stability relationships
For herbivores and insectivores, mean percent change
in species turnover from 2020 to 2100 (%Δβsim) was
negative at low elevations under low and moderate
management intensity, indicating increasing homog-
enization. At higher elevations, however, herbivores
and insectivores displayed increasing among-site
species turnover across all management scenarios.
In contrast, mean %Δβsim was positive for all man-
agement scenarios and both elevation categories for
predators. Additionally, for predators, pairwise effect
size calculations indicated negligible effects among
management scenarios within each elevation cate-
gory. The largest negative mean %Δβsim for all trophic
groups occurred in the low intensity management
scenario below 1700m (insectivores: −5.02 ± 13.26
SD; herbivores: − 4.47 ± 26.02 SD; predators:
6.71 ± 25.18 SD; Fig. 5), indicating a high degree
of homogenization. The most positive mean %Δβsim
occurred in the high intensity management scenario
at or above 1700 m for all trophic groups (insecti-
vores: 15.65 ± 20.64 SD; herbivores: 15.96 ± 116.42
SD; predators: 41.92 ± 142.14 SD). Overall, there
was greater variability in %Δβsim at higher elevations
as compared to lower elevations and predators dis-
played the greatest variability among trophic groups.
Fig. 4 Stability of species composition (βsimICV) by mean cur-
rent spatial species turnover for 2020 (βsim) grouped by trophic
group (herbivores, insectivores, and predators) and elevation
category in the central Sierra Nevada Mountains, USA
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For herbivores and insectivores, pairwise effect size
calculations indicated that low intensity management
resulted in increased homogenization as compared to
high intensity management below 1700m (herbivores
Cohen’s D: − 0.26, 95% CI: − 0.31—-0.20; insec-
tivores Cohen’s D: − 0.37, 95% CI: − 0.42–0.31).
%Δβsim was not notably different for the moderate
intensity scenario as compared to the low intensity
scenario for any trophic group or elevation category.
Additionally, insectivores were the only trophic group
that displayed higher %Δβsim values for high inten-
sity management as compared to the low intensity
management ≥ 1700m (Cohen’s D: −0.29, 95% CI:
−0.35- −0.23).
Overall, mean among-site compositional stabil-
ity (βsimICV) increased with increasing management
intensity across all elevations for both herbivores and
predators. For herbivores, the high intensity manage-
ment scenario had a mean βsimICV of 6.68 ± 5.96 SD
at low elevations and 6.76 ± 3.86 SD at high eleva-
tions. Unlike herbivores, however, βsimICV was lower
for predators at or above 1700m as compared below
1700 m under the highest management intensity
( < 1700 m: 7.73 ± 5.55 SD, ≥ 1700 m: 4.76 ± 2.89
SD). Similar to herbivores and predators, mean
βsimICV for insectivores below 1700 m was highest
for high intensity management (7.09 ± 5.99 SD) and
lowest for low intensity management (4.97 ± 5.03
SD). However, for insectivores at higher elevations,
βsimICV was highest for moderate intensity manage-
ment (5.24 ± 2.78 SD) and lowest for high intensity
management (5.01 ± 2.79 SD).
Management intensity did not significantly affect
positive relationships between mean current (2020)
among-site species turnover (βsim) and local temporal
compositional variability (TBI) for any trophic group
or elevation class. Similarly, increasing management
intensity did not change the direction of relation-
ships between mean current (2020) βsim and βsimICV
for any trophic group, however, the strength of the
relationship between βsim and βsimICV decreased with
increasing management intensity in some scenarios
(Fig.6). Specifically, high intensity management was
significantly associated with a decline in the effect
of mean βsim on βsimICV as compared to low intensity
management for herbivores and insectivores at low
elevations (herbivores coefficient: -1.05, SE: 0.25,
p < 0.001; insectivores coefficient: -0.74, SE: 0.20).
There was no significant effect of management on the
relationship between mean βsim and βsimICV for preda-
tors below 1700m. At elevations at or above 1700m,
increasing management intensity did not change the
direction or strength of the relationship between mean
βsim and βsimICV for either herbivores or insectivores.
In contrast, for predators, high management intensity
was associated with a decreased effect of mean βsim
on βsimICV at high elevations (coefficient: −0.48, SE:
0.22, p = 0.026). These relationships were only found
for high intensity management. Moderate intensity
management had no effect on relationships between
mean βsim on βsimICV for any trophic group at either
elevation category.
In contrast to significant effects of management
found for relationships between mean βsim and βsimICV,
there were no significant differences among manage-
ment scenarios for predicting TBIloss from mean βsim
for any trophic group below or above 1700m. This
result indicates that areas of high current (2020)
Fig. 5 Mean percent change in spatial species turnover (%Δ
βsim) ± standard deviation as a function of management inten-
sity for three trophic groups and two elevation categories
(< 1700 and ≥ 1700m) in the central Sierra Nevada Mountains,
USA
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spatial species turnover continued to be associated
with reduced temporal compositional variability
associated with species losses independent of altered
management intensity.
Discussion
Using a multi-metric approach, we demonstrated
distinct differences along a topographic gradient and
among management scenarios and trophic groups for
patterns of beta diversity across space and through
time and for key diversity-stability relationships in
an era of climate change. In general, regions that
had higher current among-site species turnover also
displayedhigher among-site compositional stabil-
ity through time and reduced temporal composi-
tional variability attributed to species losses; how-
ever, management intensity and elevational gradients
influenced the strength of these relationships. These
observed differences may have important implica-
tions for ecosystem function.
In montane systems, species reorganization in
response to climate change is particularly evident
due to topographic complexity, including steep eleva-
tional gradients (Elsen & Tingley 2015). On moun-
tain ranges in western North America, species are
expected to shift upslope in response to warming tem-
peratures or shift to areas where micro- and macrocli-
mate conditions are likely to remain stable for longer
periods of time, such as north-facing slopes or within
disturbance refugia (Morelli etal. 2020). Range shifts
have been evidenced for multiple species and trophic
groups in temperate montane systems (Moritz et al.
2008; Tingley etal. 2012). Consistent with previous
studies, we observed changes in patterns of diversity
through time along a substantial elevational gradient.
This gradient represented large changes in dominant
Fig. 6 Stability in among-site species composition (βsimICV)
from 2030 to 2100 by mean current (2020) species turno-
ver (βsim) grouped by management scenario for three trophic
groups (herbivores, insectivores, and predators) at elevations
below 1700m in the central Sierra Nevada Mountains, USA
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Landsc Ecol (2025) 40:84 Page 13 of 19 84
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forest types in snow- versus rain- dominated precipi-
tation regimes. For all trophic groups, local-scale
changes in patterns of diversity were driven by spe-
cies losses at low elevations and species gains at high
elevations. Additionally, regional species composi-
tion for herbivores and insectivores trended toward
homogenization at lower elevations, although some
of these patterns were modified by management
intensity (see below). Such patterns in shifting spe-
cies distributions are indicative of changing climatic
conditions and may reduce the diversity of functions
and services provided by ecosystems (Savage & Vel-
lend 2014; Daru etal. 2021). At lower elevations, in
rain-driven vegetation communities, management
intensity had the greatest impact on mitigating cli-
mate-driven trends in and relationships pertaining to
wildlife species diversity. In contrast, in high eleva-
tion snow-driven systems, climate-driven changes
leading to emerging patterns of homogenization or
compositional species loss were not evidenced in
our study and therefore the mitigating influence of
increasing management intensity was less apparent.
These patterns and trends differed among trophic
groups, however. Trends in herbivore and insecti-
vore diversity were sensitive to changes in eleva-
tion and management. In particular, percent change
in among-site species turnover over time switched
from negative to positive on average with increased
elevation and management intensity. Due to their reli-
ance on vegetation composition and structure for a
variety of life history traits and activities, herbivores
and insectivores may be particularly sensitive to habi-
tat changes, such as shifting cover types, forage, and
phenology (Thackeray etal. 2017). In contrast, for
predators, average percent change in among-site spe-
cies turnover remained positive despite fluctuating
elevation or management intensity. Predators also dis-
played different patterns in among-site compositional
stability, with decreased stability at higher elevations
as compared to herbivores and insectivores which
displayed increased stability at higher elevations.
Our predator group included top predators (mountain
lion) as well as mesocarnivores (e.g., fox and fisher),
birds, and reptiles. These species use a variety of hab-
itats and therefore patterns of diversity may be less
affected by changing landscape patterns, particularly
at lower elevations (Brechtel etal. 2019).
Diversity can stabilize ecosystems in many ways,
including by promoting the persistence of important
ecosystem services (García-Palacios et al. 2018).
Nevertheless, accelerated intensity and frequency
of disturbance and the threat of ecological trans-
formation projected under climate change remains
an understudied potential challenge to important
diversity-stability relationships across spatial scales
(De Boeck etal. 2018). In a high emissions future,
our results indicated that local species assemblages
increasingly became more dissimilar with time, indi-
cating that the types and abundance of species are
shifting in response to changing environmental con-
ditions. Nevertheless, in areas where spatial species
turnover was initially high, among-site compositional
stability remained high through time. This result was
consistent across trophic groups, elevation classes,
and management intensities. Relevant to transform-
ing ecological systems, areas that sustain high spatial
beta diversity may facilitate asynchronous species
responses to disturbances which can promote stabil-
ity of aggregate properties, such as biomass, and eco-
system function through time (Wilcox et al. 2017;
Lamy et al. 2021; Wisnoski et al. 2023). Although
important aggregate properties might be maintained
across larger spatial scales, functions and services
provided at local scales may still fluctuate as spe-
cies assemblages adjust to changing environmental
conditions. In contrast to among-site compositional
stability, our study revealed that local temporal com-
positional variability increased with increasing spatial
species turnover. Disturbance can alter species com-
position at local levels by increasing habitat hetero-
geneity, allowing colonization by new species (Belote
et al. 2012). Our findings support the notion that
high among-site diversity can help compensate for
species losses through colonization and species sort-
ing (Loreau etal. 2003). Consistently, we found that
higher spatial species turnover was associated with
lower temporal compositional variability associated
with species losses for all trophic groups and manage-
ment scenarios.
Biodiversity conservation has not traditionally
been a target of natural resource management in west-
ern North America, however, and this lack of focus
on biodiversity could have unintended detrimental
impacts on overall forest health and function (Lin-
denmayer etal. 2000; Thorn et al. 2017). For exam-
ple, snags and coarse woody debris, often removed
in wildfire-targeted forest treatments, are important
resources for several wildlife species in forested
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systems (Riffell et al. 2011; Sullivan et al. 2021).
Furthermore, forest management strategies that help
facilitate the development of old growth forest across
large extents may have a negative impact on the prev-
alence of early successional habitat and early-seral
dependent species assemblages, reducing regional
diversity (Swanson et al. 2010). In contrast, forest
management can have positive impacts on biodiver-
sity and ecosystem function. Silviculture treatments
to reduce wildfire spread and severity can increase
landscape heterogeneity and structural forest diver-
sity, thereby increasing local- to regional-scale pat-
terns of diversity (Carey 2003; Verschuyl etal. 2011;
Oettel & Lapin 2021). In our study, increasing the
footprint and frequency of management led to posi-
tive percent change in spatial species turnover over
time, whereas minimal intervention led to increased
homogenization at low elevations where composi-
tional change associated with species losses was high.
These results suggest that forest management has the
potential to conserve important ecosystem services
through biodiversity conservation, particularly if bio-
diversity conservation becomes a target to guide man-
agement actions (Carey etal. 1999; Hagan & Whit-
man 2006; Sitters & Di Stefano 2019; Zeller etal.
2023).
Although increasing management intensity was
associated with increased among-site compositional
stability and reduced likelihood of species homog-
enization associated with species losses at lower
elevations, it also contributed to large positive gains
in diversity at high elevations. Species gains at high
elevations with climate change may have both unin-
tended positive and negative consequences to eco-
system services. Novel species interactions resulting
from species gains and diversifying systems may
lead to local or regional extirpation of some species
through competition or can facilitate the coexistence
of species through environmental modifications and
mutualisms (Young et al. 2001). Our modeling was
unable to assess the impacts of novel interactions and,
overall, there remains a need to better understand how
community-level reorganization may impact future
patterns of diversity (Alexander etal. 2015). Addi-
tionally, we found that increasing management inten-
sity weakened positive relationships between mean
spatial species turnover and among-site compositional
stability for herbivores and insectivores at low eleva-
tions and for predators at high elevations. Decreased
contribution of diversity to among-site compositional
stability under high intensity management may either
indicate a decoupling of this important process or a
relaxing of this process as management becomes a
more prominent driver of landscape conditions and
emerging patterns of diversity at larger spatial scales
(Kuuluvainen etal. 2021). In contrast, management
intensity did not significantly alter positive relation-
ships between spatial species turnover and local vari-
ability in patterns of diversity through time. This lack
of an effect of management may indicate that compo-
sitional change in local communities is more strongly
driven by disturbance and environmental fluctuations
associated with climate change. Recognizing both
the importance of beta diversity and management
for promoting regional compositional stability may
help managers strike a balance to conserve existing
healthy, diverse communities and to restore diversity
where it may be lacking.
Our results should be interpreted through a lens of
species presence potential given habitat conditions,
rather than true species presence, as we were not
able to model individual species dispersal or adap-
tive capacity in response to changing environmental
conditions. This lack of empirical data for validating
the modeled habitat-species relationships, in addition
to our inability to model immigration into the study
area and restriction of analyses to breeding habitat,
could have led to over- or underestimation of spe-
cies presence. Nevertheless, evidence from empiri-
cal studies of climate- and disturbance-driven range
shifts of wildlife and their habitat support our find-
ings for shifting spatial patterns of diversity (Forister
etal. 2010; Rowe etal. 2015; Mamantov etal. 2021),
and we believe the trends we found are robust to these
potential shortcomings. However, both drivers and
proximate and ultimate consequences of large-scale
range shifts are highly complex and require further
study. Furthermore, LANDIS-II is intended to gen-
erate simulations over large landscapes and time
scales, necessitating model simplification that, while
informed by the best current knowledge of system
dynamics, limits our ability to capture all ecological
interactions and processes that could influence result-
ing patterns of diversity. By assessing wildlife diver-
sity in its complexity, we were able to detect mean-
ingful trends that could aid in making management
decisions. Climate change is altering the spatial scale
at which management plans and goals are developed
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to achieve long-term effectiveness (Gaines et al.
2022). After completing independent analyses of
shifting trends in species richness (alpha diversity) for
the Tahoe Central Sierra planning area, our team felt
we may be missing key information that could explain
shifting patterns of diversity as impacted by climate
change, disturbance, and management (Zeller etal.
2023). The beta diversity indices we used for this
research helped illustrate how underlying environ-
mental processes drive regional patterns and trends in
wildlife composition through space and time. Unlike
alpha diversity, beta diversity can provide insight into
how patterns of species composition at local scales
influences changes in larger metapopulations and
communities (Ruhí etal. 2017; Fontana et al. 2020).
Conservation and management decisions made at a
local scale remain important, however, losing sight
of how those activities influence greater communi-
ties hinders the ability of managers to detect shifts in
overall ecosystem health and function and may even-
tually lead to loss in biodiversity over time (Messier
etal. 2019; Jaeger etal. 2022). Beta diversity indices
used in this study, such as temporal species loss and
compositional stability, can provide important insight
into where and how fast communities may reorgan-
ize in response to acute (e.g., wildfire and manage-
ment) and prolonged (e.g., climate change) distur-
bances (Mori et al. 2018). This information can be
used to help managers better determine when, where,
and what management actions are needed to support
healthy functioning landscapes.
Natural disturbances are important drivers of bio-
diversity, however, direct human impacts and climate
change are altering ecosystem conditions and their
responses to natural disturbances, affecting overall
ecosystem function (Kelly etal. 2020). Widespread,
high severity disturbances are becoming more com-
mon and have been linked to increasing regional to
landscape-level homogenization of ecological com-
munities (Weeks etal. 2023). Restoring or maintain-
ing low to moderate severity disturbance to ecological
systems can promote maintenance of biodiversity and
reduce the likelihood and frequency of high sever-
ity events (Viljur etal. 2022). Furthermore, if these
actions specifically target biodiversity as a manage-
ment goal, they may also foster the ability of eco-
logical communities to remain resilient in the face of
climate change and associated ecological transforma-
tion (Oliver etal. 2015; Hisano etal. 2018). To our
knowledge, this is the first study to investigate how
compounding effects of climate change, natural-,
and human-caused disturbance impact relationships
between species compositional structure and compo-
sitional stability. Our results indicate that composi-
tional diversity will continue to play an important role
in supporting community structure through changing
climatic conditions. Additionally, management can
create mosaic environments that promote diversity
for multiple trophic groups when biodiversity is rep-
resented in its complexity and carefully considered in
designing management approaches.
Acknowledgements This research was supported in part by
the USDA Forest Service, Rocky Mountain Research Station,
Aldo Leopold Wilderness Research Institute as well as the
USDA Forest Service, Pacific Southwest Research Station. The
findings and conclusions in this publication are those of the
authors and should not be construed to represent any official
USDA or US Government determination or policy.
Author contributions PM initiated the study, coordinated
funding support, and guided study design. NP and SF ran the
LANDIS-II modeling and provided the crosswalks to CWHR.
KH performed beta diversity and stability analyses and drafted
the initial manuscript. All authors contributed substantially to
manuscript drafts and approved of the final version.
Funding This research was supported in part by the Cali-
fornia Tahoe Conservancy (CTA 19 006R, CA Proposition 68
funding), in support of the Tahoe Central Sierra Initiative.
Data availability The datasets and programming code gen-
erated or analyzed during the study are available in a public
repository on GitHub (https:// github. com/ klhef ty/ TCSI- Beta-
Diver sity- Analy sis) and as supplementary information accom-
panying this manuscript.
Declarations
Competing interests The authors declare no competing inter-
ests.
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