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Abstract and Figures

Human engineering of the outdoors led to the development of the indoor niche, including home construction. However, it is unlikely that domicile construction mechanics are under direct selection for humans. Nonetheless, our preferences within indoor environments are, or once were, consequential to our fitness. The research of human homes does not usually consider human evolution, and, therefore, we are without previous predictions about indoor climate preference. We worked with citizen scientists to collect indoor climate data from homes (n = 37) across the USA. We then compared these data to recent global terrestrial climate data (0.58 grid cells, n = 67 420) using a climate dissimilarity index. We also compared some climate-related physiological parameters (e.g. Thermoneutral zone (TNZ)) between humans and a selection of non-human primates. On average, our study homes were most similar in climate to the outdoor conditions of west central Kenya. We found that the indoor climates of our study homes largely matched the TNZ of humans and other primates. Overall, we identified the geographical distribution of the global outdoor climate that is most similar to the interiors of our study homes and summarized study home indoor climate preferences.
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royalsocietypublishing.org/journal/rsos
Research
Cite this article: Just MG, Nichols LM, Dunn RR.
2019 Human indoor climate preferences
approximate specific geographies. R. Soc. open
sci. 6: 180695.
http://dx.doi.org/10.1098/rsos.180695
Received: 1 May 2018
Accepted: 11 February 2019
Subject Category:
Biology (whole organism)
Subject Areas:
ecology/biogeography
Keywords:
climate dissimilarity, human niche construction,
human associates, thermal comfort,
indoor biome
Author for correspondence:
Michael G. Just
e-mail: mjust@ncsu.edu
Electronic supplementary material is available
online at https://dx.doi.org/10.6084/m9.figshare.
c.4428419.
Human indoor climate
preferences approximate
specific geographies
Michael G. Just1, Lauren M. Nichols2
and Robert R. Dunn2,3
1
Department of Entomology and Plant Pathology, and
2
Department of Applied Ecology,
North Carolina State University, Raleigh, NC, USA
3
Center for Macroecology, Evolution and Climate, University of Copenhagen, Copenhagen,
Denmark
MGJ, 0000-0003-2493-9269; RRD, 0000-0002-6030-4837
Human engineering of the outdoors led to the development of
the indoor niche, including home construction. However, it is
unlikely that domicile construction mechanics are under
direct selection for humans. Nonetheless, our preferences
within indoor environments are, or once were, consequential
to our fitness. The research of human homes does not
usually consider human evolution, and, therefore, we are
without previous predictions about indoor climate
preference. We worked with citizen scientists to collect
indoor climate data from homes (n¼37) across the USA. We
then compared these data to recent global terrestrial climate
data (0.58grid cells, n¼67 420) using a climate dissimilarity
index. We also compared some climate-related physiological
parameters (e.g. thermoneutral zone (TNZ)) between humans
and a selection of non-human primates. On average, our
study homes were most similar in climate to the outdoor
conditions of west central Kenya. We found that the indoor
climates of our study homes largely matched the TNZ of
humans and other primates. Overall, we identified the
geographical distribution of the global outdoor climate that is
most similar to the interiors of our study homes and
summarized study home indoor climate preferences.
1. Introduction
Climate plays an important role in the life history of most
organisms, and the influence of climate on the ecology,
evolution and distribution of organisms has been the subject of
many thousands of studies. Similarly, outdoor climates
themselves have been the subject of a rich body of work, both
in terms of current climate, projected future climate and
modelled or measured historic climates. Yet, somehow, the
&2019 The Authors. Published by the Royal Society under the terms of the Creative
Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits
unrestricted use, provided the original author and source are credited.
relationship between humans and climate, particularly the climate in the ecological realm we spend the
most time in, our homes, remains poorly studied, particularly with regard to the ecology and evolution
of humans and the many thousands of species that live alongside us [1,2].
Dawkins coined the term extended phenotype to describe the extent to which an organism’s genes
encode not only its body and behaviour but also the ways in which that organism might manipulate
the environment [3]. The termite’s nest is part of its extended phenotype [4] and is mediated both by
genes associated with behaviour and the rules those genes influence, just as the warren of a mouse is
part of its [5]. Recent work has even begun to understand the individual genes associated with deer
mice (Peromyscus spp.) and when they build one type of warren relative to another [6]. But what
about humans? It would be difficult to convincingly argue that the behaviours leading to the
construction of human houses are under direct selection. Many humans (the authors of this paper
included) could not build a modern house if their life depended on it, yet we persist. However, the
issue may be more subtle than it at first seems; human preferences influence human houses. Our
houses are built to reflect both comfortable temperatures and levels of humidity [7,8]. If our house is
too hot or cold, we modify it in such a way as to produce more heat and vice versa [9]. However, for
thousands of years before air conditioning, we also modified conditions through construction or
placement of homes that buffered outdoor climates with passive measures such as sun shading,
thermal mass and ceiling architecture, to both to make them liveable and to make them comfortable
[10,11].
For ectotherms, a large body of the literature considers how individual organisms alter their climate
[12]. Species seek favoured climates or employ body postures that alter the temperature to which they are
exposed [13– 16]. In social insects, some species even alter the climate around them, and particularly their
brood, whether through collective behaviours (e.g. honey bees [17]) or through the constructions the
behaviours create (e.g. nests [18]). Similar phenomena are reported for mammals, but often
anecdotally, especially for primates including humans [19]. The relationship of humans with climate is
complex [20,21]. We thermoregulate [22], acclimate [23], and, over time, we have even adapted in as
much as individual human lineages appear to demonstrate physiological and anatomical differences
associated with their historic climates [24]. Yet, the defining way in which we have responded to
outdoor climate, since the advent clothing, no less than 20 000 years ago, is to modify the climate we
are exposed to in order to maximize thermal comfort [25].
A rich literature considers the many proximate factors that influence thermal comfort. Thermal
comfort can be influenced by culture [11,26], by wind speed and humidity [2730] and mean radiant
temperature [31,32]. This literature suggests that the many ways in which the climate people prefer for
their homes might be modulated and why. But what these do not change is the reality that thermal
comfort itself, evolved.
What do we favour about these indoor climatic conditions? Are they similar to the climate of our
ancestors? Which (outdoor) climate are we attempting to reconstruct when we turn the heat up or
down? These questions seem to have been given little consideration, perhaps for two reasons. First,
there is a paucity of reported indoor climate data across seasons for occupied homes, which would
allow direct comparison with outdoor climates except where specific house types are being compared
(e.g. traditional versus modern homes [26,27]). Second, the people who study indoor environmental
quality (e.g. homes and their interior climates) do so in the context of creating interior spaces that
promote comfort and productivity rather than in an ecological or evolutionary context [33,34].
Understanding the climates humans construct in light of human ecology and evolution has relevance
not only to understanding why we build homes the way we do (and how we might make more
reasoned decisions in the future), but also the climate that we create for other organisms indoors. The
indoor biome is one of the most rapidly growing biomes on Earth [35], yet its climatic features have
not been well characterized with regards to species ecology, nor have they been compared to other,
outdoor climates. Such a comparison is necessary in order to understand which climates we have
replicated indoors and which species might be most predisposed, in terms of climate, to live with us
in the future, whether wanted or unwanted. As many as several hundred thousand species have been
found living in homes [1,2], and the question of the climate that these species inhabit is relevant to
the basic biology of a broad swathe of life.
Here, we worked with citizen scientists to record the climate within homes across the USA. We first
characterize the indoor climates of these homes, then we compare these indoor climates with what is
known about the climatic tolerances of non-human primates, and finally, we identify specific
geographies from across the globe whose climate is most similar to the observed indoor climates. In
considering which (global) outdoor climates these North American homes are most similar to, we
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2
argue that there are two consequences of the conditions that we prefer in our homes. First, the climates
we prefer have strong effects on global energy usage and how that usage varies geographically. Second,
and perhaps less obviously, in constructing our homes and modulating their climate as an extension of
our phenotype (and to some extent culture) we might also recreate specific climates for other organisms,
favouring the subset of species that prefer the same climates as we do [35].
2. Material and methods
2.1. Climate datasets
With the assistance of citizen scientists, we collected indoor and outdoor climate data from homes from
each state of the USA and Washington, DC using a temperature (8C) and relative humidity (%) data
logger (iButton model DS1923-F5, Maxim Integrated Products, Inc., Sunnyvale, CA, USA) that is
commonly used in ecological studies [36]. For indoor climate measurements, participants were
instructed to place the data logger on a surface with a low risk of physical disturbance, and away
from any air vents, windows or direct sunlight (e.g. shelf, bookcase). Participants were asked to place
the outdoor climate logger in a location that was disturbance free and shaded. The data collection
period was February 2013– April 2014, and temperature and humidity were recorded once per hour.
During initial data processing, prior to analysis, we removed homes that did not have records from
summer, winter and, at least, spring or autumn; 37 homes were retained (additional information on
study homes available as electronic supplementary material, table S1). To align the indoor air
moisture variable with that of the global outdoor data, we calculated vapour pressure (hPa) from
indoor temperature and relative humidity observations using the August– RocheMagnus equation
[37]. Home climate data were converted to monthly averages prior to analyses. We examined the
relationship between indoor and outdoor home temperatures with linear regression, fitting regressions
for both vapour pressure and temperature by season. All analyses were performed in R [38] (version
3.3.2; http://www.R-project.org). This research was approved by the NC State University IRB review
board under IRB Protocol 2177. We received written consent from all participants.
Global, outdoor climate data were acquired from the University of East Anglia Climatic Research
Unit’s Time-Series Version 3.21 High Resolution Gridded Data [39] (CRU TS3.21; http://catalogue.
ceda.ac.uk). This dataset is constructed from monthly observations from terrestrial meteorological
stations from across the globe. Station anomalies are interpolated to 0.58grid cells (n¼67 420
terrestrial cells excluding Antarctica) and combined with an existing climatology [40] to derive
absolute monthly values. We used the 2012 CRU TS3.21 monthly air temperature (8C) and vapour
pressure (hPa) data for our analyses.
2.2. Climate dissimilarity
We calculated the dissimilarity between North American indoor and global outdoor climates, using the
climatic parameters air temperature (8C) and vapour pressure (hPa), to determine if indoor climates
approximated outdoor climates of specific geographies. For our dissimilarity analyses, we used six
climate variables: minimum mean air temperature and mean vapour pressure for winter, mean air
temperature and mean vapour pressure for spring/autumn, and maximum air temperature and mean
vapour pressure for summer. Seasons were defined as follows for the Northern and Southern
Hemispheres, respectively: December– February (winter/summer), MarchMay (spring/autumn),
JuneAugust (summer/winter), September November (autumn/spring). Spring and autumn were
analysed as one season, averaging spring and autumn values as needed. Air temperature and air
moisture are often-used climatic variables when considering indoor climate and human thermal
comfort [29,41]. These parameters have also been used in studies of climate analogues [42].
We used a standardized Euclidian distance to compute a climate dissimilarity index [42,43] between
each home and global grid cell (equation (2.1)), using the climate variables described above.
Cij ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
X
6
k¼1
(gkj hki)2
S2
ki
v
u
u
t,ð2:1Þ
where Cis the climate dissimilarity index between each indoor iand outdoor jlocation. Where kis the
climate variable (n¼6), his the mean of the indoor climate variable kat i,gis the mean outdoor climate
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3
variable kat j, and S2
ki is the standard deviation of the indoor climate variable. Climate dissimilarity
indices are a common tool used to compare climates separated by space and/or time and to find the
climate that is most or least similar to a focal climate [42,4446]. We also calculated the root mean
square errors for temperature and vapour pressure between each home and global grid cell, and the
methods and results of these analyses can be found in the electronic supplementary material,
appendix A.
3. Results
3.1. Indoor climates
The mean maximum temperature in the summer for the 37 homes ranged from 22.22 to 34.638C, with a
mean of 27.27 +0.468C (standard error of the mean); mean vapour pressure ranged from 10.22 to
25.28 hPa with a mean of 16.15 +0.46 hPa ( figure 1). The mean minimum temperature in winter
ranged from 8.38 to 228C, with a mean of 16.44 +0.528C, and mean vapour pressure ranged from
4.98 to 22.33, with a mean of 8.75 +0.54 hPa. The mean temperature of spring/autumn ranged from
17.52 to 25.378C, with a mean of 21.51 +0.288C, and mean vapour pressure ranged from 8.26 to
23.59 hPa, with a mean of 12.82 +0.46 hPa.
Outdoor air temperature was a significant predictor of indoor home temperature by season, but the
strength of these associations was modest (table 1). The relationship between outdoor and indoor
temperature was especially weak in winter (t¼2.88, adjusted R
2
¼0.04, p,0.001). Associations of
outdoor and indoor home vapour pressure were generally stronger than the same comparisons for
temperature (table 1). The weakest relationship between outdoor and indoor home vapour pressure
was found in summer (t¼7.69, adjusted R
2
¼0.30, p,0.001).
3.2. Most similar indoor and outdoor climates
We identified the outdoor location(s) with the most similar climate for each of our study homes (table 2).
The indoor climate from the Oregon home, for example, had the smallest observed Cand was a close
match (C¼0.3812) with a grid cell in Kenya (0.258N, 35.258E). By contrast, the indoor climate for
the Missouri home had the greatest minimum C(3.765) for its most similar outdoor climate (1.758N,
35.258E) which was also located within Kenya. To generalize the climate similarities, we also
considered the 100 most similar outdoor climates for each home. The Hawaii home had the lowest
mean C(0.900 +0.014) and these global grid cell centres that were most often located in Brazil
(figure 2) and the Missouri home also had the greatest minimum mean C(4.120 +0.017), and the
locations of these global grids most often occurred in Ethiopia.
Considering all global cells (n¼67 420), the location with the least similar climate to the mean North
American indoor climate was located within northern Greenland (79.758N, 39.258W; C¼39.874). In
other words, to achieve the indoor conditions found in North America, someone in Greenland would
have to alter indoor conditions relative to outdoor conditions more than anywhere else on Earth.
Conversely, the location with the most similar climate was located in west central Kenya (1.258N,
35.758E; C¼2.938). In west central Kenya, outdoor conditions are essentially the same as the mean
conditions created inside homes in North America.
We were interested in identifying potential global, outdoor locations from which the species
associated with North American homes might be most expected to have come. To this end, we used
the overall mean climatic dissimilarity metric (C) from our study homes to identify the 100 (of 67 420)
most similar global grid cells (figure 3). The value distributions of the climate variables used in the
climate dissimilarity index can be viewed in figure 1.
4. Discussion
Here, we present data on observed indoor climate from homes across the North America (figure 1).
Indoor environments are important for humans; the average person in the USA spends, for example,
less than 10% of their time outdoors [47]. In spite of numerous reports of human thermal preferences
inside buildings and codified climatic prescriptions (e.g. ASHRAE Standard 55) for construction of
interior spaces, data on the climates actually achieved in houses, throughout the year, have not been
widely reported.
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We also identified outdoor climates from around the world that are most climatically similar (e.g. in
terms of temperature and humidity, by season) to the indoor climate of the homes we studied. North
American homes were most similar in climate to the outdoor conditions of west central Kenya (C¼
2.938). The mean maximum temperature (average of all seasons) in the North American homes was
25.358C compared with 25.068C for the conditions outdoors in west central Kenya. The mean vapour
pressure was 12.58 hPa for North American homes and was similar to the outdoor conditions in west
central Kenya (12.96 hPa).
When humans adjust the climates within their homes, it is unlikely that most are consciously
attempting to emulate the climatic conditions of some outdoor location in another country or
continent. Instead, they are almost certainly attempting to achieve climatic conditions that result in
thermal comfort. They do so to such an extent that indoor climate is no longer well correlated to
outdoor climate (table 1). Based purely on its indoor temperature and humidity, you would be
unlikely to discern whether a house from our dataset was in Wyoming or Mississippi. Of the two
climatic variables, we considered, indoor humidity was more strongly correlated with outdoor
conditions than was the case for temperature, but this correlation was weak. The extent to which
humans have decoupled indoor and outdoor climate is likely to be the most extreme in nature. Even
honeybee nests, for example, which are actively buffered from outdoor conditions, still vary in
response to outdoor conditions.
In general, mammals, including humans, have evolved the ability to regulate their body temperatures
via behaviour and autonomic responses. Human autonomic control has the capacity to maintain brain
and core temperature over a range of environmental conditions [48]. Moreover, humans acclimate
Table 1. Results of linear models evaluating indoor home climate (temperature, vapour pressure) by outdoor home climate.
season d.f. variable estimate (s.e.) tvalue adj. R
2
winter 159 temperature (8C) 0.063 (0.02) 2.88** 0.044
vapour pressure (hPa) 0.757 (0.03) 23.41*** 0.774
spring/autumn 218 temperature (8C) 0.246 (0.02) 14.13*** 0.476
vapour pressure (hPa) 5.862 (0.33) 23.44*** 0.715
summer 136 temperature (8C) 0.245 (0.03) 8.23*** 0.328
vapour pressure (hPa) 0.351 (0.05) 7.69*** 0.298
Significance levels **p,0.01, ***p,0.001.
10
indoor outdoor indoor
winter spring/autumn summer
outdoor indoor outdoor indoor outdoor
20
30
minimum temp mean temp maximum temp vapour pressure
Figure 1. Boxplots for the climatic variables air temperature (8C) and vapour pressure (hPa) by season (spring and autumn are averaged;
winter ¼purple, spring/autumn ¼orange, summer ¼green) and location. Minimum tempis the mean minimum air temperature,mean
temp is mean air temperature, maximum temp is the mean maximum air temperature and vapour pressure is the mean vapour pressure. The
indoor climate is from our study homes and outdoor climate values are from the 100 grid cells that are the most climatically similar to the
mean home indoor climate. The box plots display data range, quartiles and median with dots as outliers. Figure was generated with R
(version 3.3.2; http://www.R-project.org) package ggplot2 (version 2.2.0; http://CRAN.R-project.org/package=ggplot2).
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5
Table 2. Results of climate dissimilarity analysis between the indoor climate of a North American home (n¼37) and 67 420
global terrestrial grid cells. C
nearest
is the minimum value of the climate dissimilarity index (C) for that state. The country where
the centre (latitude and longitude) of the grid cell is located is listed as the nearest country. C
Top 100
is the mean minimum
value of C(standard error) for the 100 most climatically similar global grid cells for that state, the corresponding country
represents the most frequently observed country from the 100 most climatically grid cells.
state C
nearest
country (nearest) latitude longitude C
Top 100
country (top 100)
Alabama 2.557 Kenya 1.75 35.25 3.181 (0.022) Ethiopia
Alaska 1.273 Namibia 13.75 219.75 1.879 (0.023) Namibia
Arizona 2.146 Namibia 222.75 15.75 2.547 (0.012) Australia
Arkansas 1.221 Ethiopia 7.75 35.25 1.79 (0.019) Ethiopia
California 1.263 Namibia 221.25 14.75 1.868 (0.019) Namibia
Connecticut 2.197 Namibia 222.25 15.25 2.625 (0.018) Angola
Delaware 0.770 Angola 213.75 16.25 1.272 (0.025) Angola
Florida 1.705 Ethiopia 3.75 38.75 2.339 (0.028) Ethiopia
Georgia 1.755 Ethiopia 9.75 35.25 2.317 (0.017) Ethiopia
Hawaii 0.561 Brazil 211.25 238.25 0.9 (0.014) Brazil
Illinois 2.648 Namibia 221.75 15.25 3.249 (0.017) Angola
Kansas 2.087 Kenya 0.75 35.75 2.64 (0.016) Ethiopia
Kentucky 2.163 Kenya 1.75 35.25 2.62 (0.017) Ethiopia
Louisiana 1.258 Kenya 21.25 38.25 1.658 (0.015) Ethiopia
Maryland 3.243 Ethiopia 9.75 35.25 3.759 (0.019) Ethiopia
Massachusetts 0.736 Angola 215.75 14.75 1.329 (0.023) Angola
Michigan 2.188 Namibia 222.75 15.25 2.742 (0.014) Namibia
Minnesota 3.079 Bermuda 32.25 264.75 3.562 (0.011) Australia
Missouri 3.580 Ethiopia 12.75 37.25 4.12 (0.017) Ethiopia
Nebraska 1.745 Angola 210.75 22.25 2.347 (0.019) Angola
Nevada 3.210 Namibia 221.75 15.75 3.945 (0.027) Namibia
New Hampshire 1.898 Namibia 221.25 14.75 2.457 (0.018) Namibia
New Mexico 2.487 Ethiopia 12.75 37.25 3.1 (0.019) Angola
North Carolina 1.519 Ethiopia 10.75 35.75 1.792 (0.009) Angola
North Dakota 2.970 Namibia 221.75 15.25 3.613 (0.018) Namibia
Oklahoma 2.820 Kenya 1.75 35.25 3.387 (0.019) Ethiopia
Oregon 0.387 Kenya 0.25 35.25 1.109 (0.023) Ethiopia
South Carolina 1.967 Ethiopia 4.25 39.25 2.511 (0.021) Ethiopia
South Dakota 2.721 Namibia 221.25 14.75 3.267 (0.018) Namibia
Tennessee 1.272 Namibia 222.25 15.25 2.08 (0.021) Angola
Utah 1.982 Namibia 221.25 14.75 2.565 (0.018) Namibia
Vermont 1.719 Mexico 25.25 2106.75 2.078 (0.012) Namibia
Virginia 1.658 Ethiopia 9.75 35.25 2.328 (0.018) Ethiopia
Washington 1.870 Kenya 1.25 35.75 2.498 (0.02) Ethiopia
West Virginia 1.561 Ethiopia 10.75 35.75 1.941 (0.012) Angola
Wisconsin 1.997 Namibia 221.75 14.75 2.538 (0.016) Angola
Wyoming 3.433 Namibia 222.75 15.75 4.119 (0.023) Namibia
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6
relatively quickly to new climatic conditions [49] and the evolution of hypothalamic controlled body
temperatures, along with behavioural and cultural advances, may have allowed humans to expand
the range of climatic conditions of their niche. So why do humans expend such extraordinary expense
to maintain constant indoor climates [50] when such climates are not necessary for survival, especially
given the plasticity of human temperature acclimation (e.g. ama divers to endurance athletes)?
Probably, it is because these climates are comfortable.
In mammals, the perception of whether a climate is comfortable or not is an important driver of
climate seeking behaviour [51], as a comfortable climate produces conditions that allow an individual
country
Angola
Australia
Brazil
Ethiopia
Namibia
NA
Figure 2. Map of the USA (not to scale). Each state represents one study home (n¼37). State fill colour represents the country
(Angola, aquamarine; Australia, salmon; Brazil, light purple; Ethiopia, magenta; Namibia, green) that was identified as most frequent
from a subset of the 100 global grid cells with most similar climate to each study home. States not included in the analysis are
shown in white. Map was generated with R (version 3.3.2; http://www.R-project.org) packages ggplot2 (http://CRAN.R-project.org/
package=ggplot2), mapproj (version 1.2-4; https://cran.r-project.org/package=mapproj), rgdal (version 1.2-7; https://cran.r-project.
org/package=rgdal) and sp (version 1.2-4; https://cran.r-project.org/package=sp). State boundaries (5 m resolution) were obtained
from the US Census Bureau (https://www.census.gov/geo/maps-data/data/cbf/cbf_state.html).
–50°
–100° 0° 100°
C
30
20
10
50°
latitude
lon
g
itude
Figure 3. Map depicting the climate dissimilarity index (C) between the mean indoor climate of the North American homes (n¼37;
20132014) and the outdoor climate of terrestrial 0.58global grid cells (n¼67 420; 2012). Dissimilarity increases as Cincreases
(yellow to blue). Cells depicted in black are those grid cells with the climatic conditions most similar to the average North American
home in terms of temperature and humidity (n¼100). Map was generated with R (version 3.3.2; http://www.R-project.org)
packages ggplot2 (version 2.2.0; http://CRAN.R-project.org/package=ggplot2), rgdal (version 1.2-7; https://cran.r-project.org/
package=rgdal), sp (version 1.2-4; https://cran.r-project.org/package=sp), and rworldmap (version 1.3-4; https://cran.r-project.org/
package=rworldmap), which uses Natural Earth data (version 1.4.0; http://www.naturalearthdata.com) for country borders.
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7
to remain within their thermoneutral zone (TNZ). The TNZ is the range of environmental conditions
where, for a given animal, heat loss equals gain and core body temperature is maintained [52]. When
an individual is outside of this range of conditions, the individual may adjust climatic conditions
behaviourally, physiologically or psychologically to adapt to the climatic conditions and ultimately
perceive thermal comfort [51,53]. These TNZs are mutable and may change with an individual’s
climatic history or habituation of an indoor space (i.e. the Adaptive Comfort Model), but the methods
to achieve thermal comfort remain the same [53,54]. Interestingly, the range of the mean indoor
temperature recorded by citizen scientists in their homes and the 100 most climatically similar global
grid cells (figure 3) largely fall within the TNZs (24 308C) for primates including humans [23]. A
comparison of climate-related physiological parameters between humans and a selection of non-
human primates is included in table 3. We hypothesize that indoor climates largely correspond with
our TNZ because our ancestors evolved thermal preferences that led them to favour (and ultimately
build) these climates.
Perhaps not surprising, in light of the TNZ hypothesis, the temperature people prefer overlaps with
much of the geographical area in which key events in hominid evolution and, for that matter, early
civilization occurred [48]. We hypothesize that natural selection favoured human preferences and
thermal traits that allowed human ancestors to live in those climates. However, as humans moved out
of those environments they faced new climates. Strong evidence suggests that the selective pressures
imparted by climate has altered human genomes [24,61,62]. In addition, new climates led to cultural
responses such as the use of fire for heat [63], clothing [26] and shelter [64], all of which modified the
climate to which individuals were exposed. We argue that modern temperatures in homes are a
continuation of this same effort, but the technological ability of humans to modify climate has led to
the extreme scenario, where fossil fuels are cheap, and (North American) indoor climates closely align
with TNZs. Moreover, air-conditioned buildings with closed ventilation combined with changing
indoor climatic expectations have also led to narrower ranges of human thermal comfort [30,53].
However, many questions remain. For example, do wealthy homeowners (or striving homeowners)
keep their homes colder than is preferred in hot places to display wealth (and vice versa)? Do genetic
backgrounds of homeowners influence preferred climates? How do these climates affect our health
and well-being? For example, indoor climates are less variable [65] than outdoor climates and this
reduced variability may lead to health issues such as obesity or diabetes [66,67].
Our results also offer a hypothesis about the likely origin of human home-associated species, as
indoor climates probably favour certain lineages, those pre-adapted to indoor climates. We
hypothesize that the assemblage of species that colonize our homes are likely to be those with thermal
preferences/tolerances similar to us, which is to say species from relatively dry, relatively warm
climates, including north and eastern Africa, but also much of the Middle East. Moreover, predictions
can be made about the communities of home associates through time and space, as climate, home
technologies and fortunes change. We know that climate preferences in homes differ among regions
[25,29], and the USA is probably an extreme case, where indoor climates most reflect resource
availability and culture, rather than economic and environmental costs.
Table 3. Climate-related values for select primate species. Variables include animal husbandry recommendations for temperature
(T
Husbandry
) and relative humidity (RH
Husbandry
), natural habitat temperature (T
Habitat
), normal adult body temperature (T
Body
) and
thermoneutral zone (TNZ). T
Husbandry
and RH
Husbandry
values, [55]. T
Habitat
values, Primate Info Network, Wisconsin National
Primate Research Center, University of Wisconsin Madison, accessed 10 April 2017; http://pin.primate.wisc.edu). T
Body
and TNZ
values, [23,56 60].
species T
Husbandry
(8C) RH
Husbandry
(%) T
Habitat
(8C) T
Body
(8C) TNZ (8C)
Gorilla beringei 18.3 29.4 30 70 3.9 14.5 unknown unknown
Gorilla gorilla 18.3 29.4 30 70 23 35.5 unknown
Homo sapiens n.a. n.a. n.a. 37 2530
Pan paniscus 18 22 50 60 20 30 unknown unknown
Pan troglodytes 15.6 29.4 30 70 18.5 30 37.25 17 29
Pongo abelii 18 28 30 70 17 34.2 unknown unknown
Pongo pygmaeus 18 28 30 70 18 37.5 37 unknown
royalsocietypublishing.org/journal/rsos R. Soc. open sci. 6: 180695
8
Our characterization of the indoor climate of North American homes and the identification of the
outdoor climates most similar to these homes opens a new line of inquiry. Why do we prefer these
climatic conditions? Do the climates of modern houses reflect our ancestral climates? When and where
did we evolve these modern climate preferences, and what are the contributions of genetic and
cultural evolution to these preferences? Interestingly, the majority of 100 most climatically similar
outdoor locations were located in the hot and seasonably dry northeastern Africa, a region rich in
hominid fossils and evolution [68].
As a first step, we presented a simple comparison between the indoor climate of these North
American homes and the climatic conditions experienced by some non-human primates (i.e. great
apes). We found that climatic conditions generally overlapped. However, no a priori predictions seem
to exist for which global climate we might favour in our homes, and future work should test the
simplest one, namely that we tend to attempt to recreate the conditions from which we evolved,
before we had the ability to make homes, the ones to which our physiologies are adapted.
Ethics. This research was approved by the NC State University IRB review board under IRB Protocol 2177. We received
written consent from all participants.
Data accessibility. The datasets supporting this article have been uploaded as part of the supplementary material.
Authors’ contributions. M.G.J., L.M.N. and R.R.D. conceived and designed the research and revised the manuscript; M.G.J.
analysed the data and prepared the draft manuscript. All authors gave final approval for publication.
Competing interests. We have no competing interests.
Funding. This work was supported by a National Science Foundation CAREER Award (no. 953390) to R.R.D.
Acknowledgements. We thank the citizen scientists who collected the climatic data from their homes, and Lea Shell and
Meghan Thoemmes for their help coordinating citizen science participation.
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A number of models have posited that the concomitant evolution of large brains and increased body sizes in hominins was constrained by metabolic costs. In such studies, the impact of body temperature has not been sufficiently addressed despite the well-established fact that the rates of most physiological processes are manifestly temperature-dependent. Hence, the role of body temperature in modulating the number of neurons and body size is investigated in this work by means of a simple quantitative model. It is determined that modest deviations in the body temperature (i.e., by a few degrees Celsius) might bring about substantive changes in brain and body parameters. In particular, a higher body temperature might prove amenable to an increase in the number of neurons, a higher brain-to-body mass ratio and fewer hours expended on feeding activities, while the converse applies when the temperature is lowered. It is therefore argued that future studies must endeavour to explore and incorporate the effects of body temperature in metabolic theories of hominin evolution, while also accounting for other factors such as foraging efficiency, diet and fire control in tandem.
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Totally 1160 adults living in single-family houses in Sweden participated in a questionnaire survey on subjective indoor air quality (SIAQ). Inspectors investigated the dwellings and performed home measurements (mean indoor temperature 21.4 °C, mean indoor air humidity 34.2%, mean indoor air exchange rate 0.36 ac/h and mean moisture load indoor 1.7 g/m³). Totally 15.5% perceived draught, 28.0% perceived too high room temperature, 42.4% unstable room temperature, 36.8% too low room temperature, 19.6% stuffy air, 19.8% dry air and 29.9% dust or dirt. Measured room temperature was related to perception of room temperature. Higher relative air humidity was related to perceived unstable room temperature (OR = 1.70) and too low room temperature (OR = 1.96). Higher absolute air humidity was related to too high room temperature (OR = 1.21), unstable room temperature (OR = 1.34) and too low room temperature (OR = 1.35). Higher measured relative humidity, absolute air humidity and moisture load were all associated with stuffy air and unpleasant odor (OR = 1.45–1.97). Higher air exchange rate was related to less perceived unstable room temperature (OR = 0.93). Higher U value was related to draught (OR = 1.17), too low room temperature (OR = 1.09), unpleasant odor (OR = 1.12) and dust and dirt (OR = 1.07). New concrete slab foundation was related to less stuffy air (OR = 0.39) (vs. basement). Damp foundation was associated with more stuffy air (OR = 1.44) and unpleasant odor (OR = 1.61). Window pane condensation was related to stuffy air (OR = 1.88). Moldy odor reported by inspector was related to stuffy air (OR = 1.73). Observed mold in the attic was associated with more stuffy air and unpleasant odor. In conclusion, complaints of room temperature can indicate poor thermal environment. Higher air exchange rate can create a more stable thermal sensation. Excess indoor humidity, lower degree of thermal insulation, presence of window pane condensation and indoor dampness/mold can impair SIAQ. Higher ventilation and concrete slab foundation with underlying thermal insulation can improve SIAQ.
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When questioned, people typically report that different foods are appropriate at different times of the year. That is, patterns of food consumption exhibit seasonal variations. Changes in food odour hedonics and familiarity ratings have also been reported over the course of the year, especially in those countries with marked seasonal changes in climate. The question addressed in this review is what factors help to explain these seasonal differences in food consumption. While our nutritional needs undoubtedly do differ somewhat over the course of the year, environmental (e.g., think only of changes in ambient temperature and/or humidity), physiological/perceptual (i.e., threshold changes), and psychological factors (e.g., wanting to make a healthy start in the New Year) also play a role. Taken together, though, it would appear that cultural/ritual factors, as well as the influence of increasingly-sophisticated data-driven marketing may be more important than nutritional, environmental, or physiological factors in helping to explain why it is that so many of us choose to eat different foods at different times of the year, despite the increasing availability of many foods on a year-round basis in the increasingly globalized food marketplace in many developed countries.
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Outside of pest control reports, little attention has been paid to interior ecosystems in high-latitude regions. Opportunistic sampling of live arthropods captured inside the University of Alaska Museum Fairbanks, Alaska, United States of America allowed us to describe and analyse one such interior ecosystem. We document a minimum of 77 arthropod species over 18 years. Beetles, spiders, and booklice represented 80% of the total abundance. Of those captured, synanthropes consisted primarily of fungivores and detritivores, seasonals consisted primarily of predators and omnivores, and transients consisted primarily of predators and had greater diet and species diversity than the synanthropes and transients. January was the most common month for capturing synanthropes, September for capturing seasonals, and July for capturing transients. Four synanthropic species not previously known from Alaska, which appear to have breeding populations inside the museum, were found: Dorypteryx domestica (Smithers, 1958) (Psocodea: Psyllipsocidae), Cartodere constricta (Gyllenhal, 1827), Dienerella filum (Aubé, 1850), and Corticaria serrata (Paykull 1800) (Coleoptera: Latridiidae). Three transient and one synanthrope species previously unreported from Alaska, with no evidence of breeding populations, were also found: the click beetle Danosoma obtectum (Say, 1839) (Coleoptera: Elateridae), a spider in the genus Phantyna , probably the species P. bicornis (Emerton, 1915) (Araneae: Dictynidae), two Colobopsis sp. ant specimens (Hymenoptera: Formicidae), and the synanthropic spider Oecobius cellariorum (Dugès, 1836) (Araneae: Oecobiidae).
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The majority of the world’s population now lives an urban existence, spending as much as 95% of their lives indoors. The olfactory atmosphere in the built environment has been shown to exert a profound, if often unrecognized, influence over our mood and well-being. While the traditionally malodorous stench to be found indoors (i.e., prior to the invention of modern sanitation) has largely been eliminated in recent centuries, many of the outbreaks of sick-building syndrome that have been reported over the last half century have been linked to the presence of a strange smell in the environment. At the same time, however, there is also growing evidence that consumer behavior can be manipulated by the presence of pleasant ambient odors, while various aromatherapy scents are said to improve our mood and well-being. This Anglophone review focuses primarily on indoor western urban developed spaces. Importantly, the olfactory ambience constitutes but one component of the multisensory atmosphere and ambient odors interact with the visual, auditory, and haptic aspects of the built environment. Surprisingly, the majority of published studies that have deliberately chosen to combine ambient scent with other sensory interventions, such as, for example, music, have failed to increase store sales, or to enhance people’s mood and/or well-being, as might have been expected. Such negative findings therefore stress the importance of considering multisensory congruency while, at the same time, also highlighting the potential dangers that may be associated with sensory overload when thinking about the effect of ambient smell on our well-being.
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In light of global climate change, ecological studies increasingly address effects of temperature on organisms and ecosystems. To measure air temperature at biologically relevant scales in the field, ecologists often use small, portable temperature sensors. Sensors must be shielded from solar radiation to provide accurate temperature measurements, but our review of 18 years of ecological literature indicates that shielding practices vary across studies (when reported at all), and that ecologists often invent and construct ad hoc radiation shields without testing their efficacy. We performed two field experiments to examine the accuracy of temperature observations from three commonly used portable data loggers (HOBO Pro, HOBO Pendant, and iButton hygrochron) housed in manufactured Gill shields or ad hoc, custom-fabricated shields constructed from everyday materials such as plastic cups. We installed this sensor array (five replicates of 11 sensor-shield combinations) at weather stations located in open and forested sites. HOBO Pro sensors with Gill shields were the most accurate devices, with a mean absolute error of 0.2°C relative to weather stations at each site. Error in ad hoc shield treatments ranged from 0.8 to 3.0°C, with the largest errors at the open site. We then deployed one replicate of each sensor-shield combination at five sites that varied in the amount of urban impervious surface cover, which presents a further shielding challenge. Bias in sensors paired with ad hoc shields increased by up to 0.7°C for every 10% increase in impervious surface. Our results indicate that, due to variable shielding practices, the ecological literature likely includes highly biased temperature data that cannot be compared directly across studies. If left unaddressed, these errors will hinder efforts to predict biological responses to climate change. We call for greater standardization in how temperature data are recorded in the field, handled in analyses, and reported in publications.
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Although humans and arthropods have been living and evolving together for all of our history, we know very little about the arthropods we share our homes with apart from major pest groups. Here we surveyed, for the first time, the complete arthropod fauna of the indoor biome in 50 houses (located in and around Raleigh, North Carolina, USA). We discovered high diversity, with a conservative estimate range of 32–211 morphospecies, and 24–128 distinct arthropod families per house. The majority of this indoor diversity (73%) was made up of true flies (Diptera), spiders (Araneae), beetles (Coleoptera), and wasps and kin (Hymenoptera, especially ants: Formicidae). Much of the arthropod diversity within houses did not consist of synanthropic species, but instead included arthropods that were filtered from the surrounding landscape. As such, common pest species were found less frequently than benign species. Some of the most frequently found arthropods in houses, such as gall midges (Cecidomyiidae) and book lice (Liposcelididae), are unfamiliar to the general public despite their ubiquity. These findings present a new understanding of the diversity, prevalence, and distribution of the arthropods in our daily lives. Considering their impact as household pests, disease vectors, generators of allergens, and facilitators of the indoor microbiome, advancing our knowledge of the ecology and evolution of arthropods in homes has major economic and human health implications.
Book
In the ten years since the publication of the second edition of Human Thermal Environments: The Effects of Hot, Moderate, and Cold Environments on Human Health, Comfort, and Performance, Third Edition, the world has embraced electronic communications, making international collaboration almost instantaneous and global. However, there is still a need for a compilation of up-to-date information and best practices. Reflecting current changes in theory and applications, this third edition of a bestseller continues to be the standard text for the design of environments for humans to live and work safely, comfortably, and effectively, and for the design of materials that help people cope with their environments. See What’s New in the Third Edition: • All existing chapters significantly updated • Five new chapters Testing and development of clothing • Adaptive models • Thermal comfort for special populations • Thermal comfort for special environments • Extreme environments • Weather • Outdoor environments and climate change • Fun runs, cold snaps, and heat waves The book covers hot, moderate, and cold environments, and defines them in terms of six basic parameters: air temperature, radiant temperature, humidity, air velocity, clothing worn, and the person’s activity. It focuses on the principles and practice of human response, which incorporates psychology, physiology, and environmental physics with applied ergonomics. The text then discusses water requirements, computer modeling, computer-aided design, and current standards. A systematic treatment of thermal environments and how they affect humans in real-world applications, the book links the health and engineering aspects of the built environment. It provides you with updated tools, techniques, and methods for the design of products and environments that achieve thermal comfort.
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Skin temperature detection thresholds have been used to measure human cold and warm sensitivity across the temperature continuum. They exhibit a sensory zone within which neither warm nor cold sensations prevail. This zone has been widely assumed to coincide with steady-state local skin temperatures between 32-34ᵒC, but its underlying neurophysiology has been rarely investigated. Here we employ two approaches to characterize the properties of sensory thermo-neutrality, testing for each whether neutrality shifts along the temperature continuum depending on adaptation to a preceding thermal state. The focus is on local spots of skin on the palm. Ten participants (30.3±4.8 y) underwent two experiments. Experiment 1 established the cold-to-warm inter-detection-threshold range for the palm's glabrous skin, and its shift as a function of 3 starting skin temperatures (26, 31 or 36ᵒC). For the same conditions, Experiment 2 determined a thermally neutral zone centered around a thermally neutral point in which thermoreceptors' activity is balanced. The zone was found to be narrow (~0.98 to ~1.33ᵒC) moving with the starting skin temperature over the temperature span 27.5-34.9ᵒC (Pearson r= 0.94; p<0.001). It falls within the cold-to-warm inter-threshold range (width: ~2.25 to ~2.47ᵒC) but is only half as wide. These findings provide the first quantitative analysis of the local sensory thermo-neutral zone in humans, indicating that it does not occur only within a specific range of steady-state skin temperatures (i.e. it shifts across the temperature continuum) and that it differs from the inter-detection-threshold range both quantitatively and qualitatively. These findings provide insight into thermoreception neurophysiology.
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A major challenge to understanding the genetic basis of complex behavioral evolution is the quantification of complex behaviors themselves. Deer mice of the genus Peromyscus vary in their burrowing behavior, which leaves behind a physical trace that is easily preserved and measured. Moreover, natural burrowing behaviors are recapitulated in the lab, and there is a strong heritable component. Here we discuss potential mechanisms driving variation in burrows with an emphasis on two sister species: P. maniculatus, which digs a simple, short burrow, and P. polionotus, which digs a long burrow with a complex architecture. A forward-genetic cross between these two species identified several genomic regions associated with burrow traits, suggesting this complex behavior has evolved in a modular fashion. Because burrow differences are most likely due to differences in behavior circuits, Peromyscus burrowing offers an exciting opportunity to link genetic variation between natural populations to evolutionary changes in neural circuits.
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Structures built by fungus-growing (Isoptera, Macrotermitinae) termites could be considered as an extended phenotype linked to the optimization of a climatic homeostasis and to a better protection against predators. Most of the literature regarding the impact of termites on soil properties refers to termite epigeous mounds. In spite of their abundance in African savannas, few studies deal with the properties of underground fungus-comb chambers and galleries. In this study we compare the physical and chemical properties of fungus-comb chamber wall and interconnecting gallery wall from Ancistrotermes cavithorax and relate these properties to the termite ecological requirements (soil structural stability and moisture regime). The termite workers increased the proportion of fine particles and the soil organic matter content in their constructions, as compared to the control soil. No difference was observed in C content between nest and gallery walls, but the nitrogen content was greater in the chamber wall. C:N ratio also decreased significantly from control soil to gallery wall and to chamber wall. These changes could help explain the increase in structural stability of the termite modified soil material. Soil water retention was also improved in termite constructions, and exhibited its greatest values in the chamber wall. Both termite constructions, chamber and gallery walls were very stable. Therefore, we suggested that both types of construction increased the protection against environmental hazards, such as dryness and water flow, and indirectly against predators. Despite similar data in fine particles and carbon content, chamber wall was a better buffer than the gallery wall for maintaining adequate moisture within the nest. We concluded that termite building activities vary according to the type of structure edified.