Ecology

The quality of global maps can be assessed in different ways. One way is global assessment where a single statistic is chosen to summarize the quality of the entire map: the map accuracy. For a categorical variable, this can be the probability that for a randomly chosen location on the map, the map value corresponds to the true value. For a continuous variable, it can be the RMSE, describing for a randomly chosen location on the map the expected difference between the mapped value and the true value. When a probability sample, such as a completely spatially random sample, is available for the area for which a global assessment is needed, then map accuracy can be estimated model-free (also called design-based, e.g., by using the unweighted sample mean in case of a completely spatially random sample). This circumvents modeling of spatial correlation because observations are independent by design6,9. This approach is called model-free because no model needs to be assumed about the distribution or correlation of the data: the only source of randomness is the random selection of sample units from a target population. If a probability sample is not available this approach cannot be used, and automatically the accuracy assessment approach becomes model-based10, which involves modeling a spatial process by assuming distributions and taking spatial correlations into account, and choosing estimation methods accordingly.Using naive random n-fold or leave-one-out cross-validation methods (or a simple random train-test split) to assess global model quality (usually equated with map accuracy) makes sense when the data are independent and identically distributed. When this is not the case, dependencies between nearby samples, e.g., in a spatial cluster, are ignored and result in biased, overly optimistic model assessment, as shown in, e.g., Ploton et al.5. Alternative cross-validation approaches such as spatial cross-validation5,11 that control for such dependencies are the only way to overcome this bias. Different spatial cross-validation strategies have been developed in the past few years, all aiming at creating independence between cross-validation folds5,11,12,13. Cross-validation creates prediction situations artificially by leaving out data points and predicting their value from the remaining points. If the aim is to assess the accuracy of a global map, the prediction situations created need to resemble those encountered while predicting the global map from the reference data (see Fig. 1 and discussions in Milà et al.14). This occurs naturally when reference data were obtained by (completely spatially random) probability sampling, but in other cases, this has to be forced for instance by controlling spatial distances (spatial cross-validation). Such forcing, however, is only possible when the distances in space that need to be resembled are available in the reference data. In the extreme case where all reference data come from a single cluster, this is impossible. When all reference data come from a small number of clusters, larger distances are available between clusters but do not provide substantial independent information about variation associated with these distances. Lack of information about larger distances means that we cannot assess the quality of predictions associated with such distances and cannot properly estimate global quality measures. Alternative approaches such as experiments with synthetic data15 or a validation using independent data at a higher level of integration16 would then be options to support confidence in the predictions.Another way of accuracy assessment is local assessment: for every location, a quality measure is reported, again as probability or prediction error. Such a local assessment predicts how close the map value is to newly observed values at particular locations. If the measurement error is quantified explicitly, a smoother, measurement-error-free value may be predicted10. If the model accounts for change of support10,17, predictions errors may refer to average values over larger areas such as 1 × 1, 5 × 5, or 10 × 10 km grid cells. Examples of local assessment in the context of global ecological mapping are modeled prediction errors using Quantile Regression Forests18 or mapped variance of predictions made by ensembles1,2. Neither of these examples quantifies spatial correlation or measurement error, or addresses change of support, as it is known from other modeling frameworks19. By omitting to model the spatial process, the local accuracy estimates as presented in the global studies that motivated this comment are disputable.The difference between global and local assessment is striking, in particular for global maps. A global, single number averages out all variability in prediction errors, and obscures any differences, e.g., between continents or climate zones. It is of little value for interpreting the quality of the map for particular regions. More

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In the version of this article initially published, there were mistakes in affiliations 1, 2 and 6. The corrected affiliations should read as follows: 1. Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China; 2. Ministry of Education Ecological Field Station for East Asian Migratory Birds, Department of Earth System Science, Tsinghua University, Beijing, China; 6. Department of Geography, Department of Earth Sciences, and Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong, China. The affiliations have been corrected in the HTML and PDF versions of the article. More

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Data collection of flowering and fruiting phenologyMonthly reproductive phenology data recorded over 35 years (from April 1976 to September 2010) were collected from the Bulletin Fenologi Biji Benih dan Anak Benih (Bulletin of Seed and Seedling Phenology), which was deposited at the FRIM library. The bulletin reported seed and seedling availabilities and the flowering and fruiting phenology of trees at several research stations in Malaysia. The present study collected flowering and fruiting records of trees grown in FRIM arboretums located approximately 12 km northwest of Kuala Lumpur, Malaysia (latitude 3°24 ‘N, longitude 101°63 ‘E, elevation 80 m). There are both dipterocarp and non-dipterocarp arboretums in FRIM, both of which were founded in 1929. These arboretums preserve and maintain living trees for research and other purposes. Each month, three research staff members of FRIM with sufficient phenology monitoring training made observations with binoculars to record the presence of flowers and fruits on trees of each species on the forest floor from April 1976 to September 2010. The phenological status of the trees was recorded as flowering during the developmental stages from flower budding to blooming and as fruiting during the developmental stages from the occurrence of immature fruit to fruit ripening. Because only one or two individuals per species are grown at the FRIM arboretums, the flowering and fruiting phenology were monitored using these individuals. The resultant flowering and fruiting phenology data included a time series of binary data (1 for presence and 0 for absence) with a length of 417 months.The original data included 112 dipterocarp and 240 non-dipterocarp species. We excluded 17 dipterocarps and 125 non-dipterocarp species based on the following five criteria for data accuracy.

1.

Percentage of missing values is ≤50%: If the monthly flowering or fruiting phenology data of a species included a substantially large number of missing values ( >50%), the species was excluded.

2.

Stable flowering period: We considered an observation to be unreliable if the flowering period was significantly different among flowering events (if the coefficient of variation in the flowering period was larger or equal to 1.0).

3.

Flowering period is shorter than or equal to 12 months: we considered an observation to be unreliable if the flowering period was longer than 12 months because it was unlikely that the same tree would flower continuously for longer than 1 year.

4.

The flowering and fruiting frequencies were not significantly different between the first and second half of the census period: when the flowering frequency was zero for the first half of the observation period but was larger than 0.1 for the second half of the observation period, or when the flowering frequency was zero for the second half of the observation period but was larger than 0.1 for the first half of the observation period, we removed these species because data are not reliable (e.g., physiological conditions may have changed significantly). We adopted the same criteria for the fruiting phenology data.

5.

We removed overlapping species, herb species, and specimens with unknown species names.

After removing unreliable species based on the five criteria explained above, we obtained 95 dipterocarp and 115 non-dipterocarp species (Supplementary Data 1). We used these species for further analyses. It is unlikely that our final data includes trees that were replaced by young trees during the census period because newly planted seedlings do not flower over 20–30 years until they are fully grown to the reproductive stage ( >20–30 cm DBH)45.Detection of seasonality in reproductive phenologyTo compare the flowering and fruiting phenology seasonality among different families, nine families that included at least five species were used. The number of flowering or fruiting events was counted for each month from January to December during a census, and then the frequency distribution was drawn as a histogram. Similarly, we also generated a histogram for the seed dispersal month, which was calculated as the month when fruiting ended (i.e., when the binary fruiting phenology data changed from one to zero).Classification of phenological patternsTo classify the phenological patterns, we performed time-series clustering using the R package TSclust46 with the hierarchical clustering method based on the Dynamic Time Warping distance of the flowering phenology data of each species. For this analysis, time points at which there were missing values for at least one species were excluded. Because of the large number of missing values in non-Dipterocarpaceae species, we performed time-series clustering only for the Dipterocarpaceae species based on 394 time points in total. The number of phenological clusters was estimated based on AIC, as explained below.Climate dataDaily minimum, mean, and maximum temperatures and precipitation data monitored at the FRIM KEPONG (3° 14’ N, 101° 42’ E, elevation 97 m) weather station were provided by the Malaysian Meteorological Department. We used the daily minimum temperature for our analysis because there were fewer missing values compared to the numbers of missing daily mean and daily maximum temperature values. The periods in which climate data were available were from 1 March 1973 to 31 March 1996, and from 23 July 1997 to 20 April 2005. We removed periods in which there were missing values spanning longer than 5 days. When the range of missing values spanned a period shorter than 3 days, we approximated these missing values using the mean minimum temperatures recorded on the adjacent three days. Although solar radiation data were not available for our study, the use of precipitation is sufficient for model fitting because there is a significant negative correlation between solar radiation and precipitation in Southeast Asia47.Climate data generated by GCMsAs the future climate inputs, we used bias-corrected climate input data from 1 January 2050 to 31 December 2099, with a daily temporal resolution and a 0.5° spatial resolution, provided by the ISI-MIP project48; these data are based on the Coupled Model Intercomparison Project Phase 5 outputs from three GCMs: GFDL–ESM2M, IPSL–CM5A-LR, and MIROC5. To compare the flowering phenology between 1976–1996 and 2050–2099, bias-corrected GCM data from 1 May 1976 to 31 March 1996, were also used. This period (1 May 1976–31 March 1996) is consistent with the period used for model fitting. We selected daily minimum temperature and precipitation time series from the 0.5° grid cells corresponding to the study site for phenology monitoring at FRIM. To compare flowering phenology among regions, we also used the same set of data from three other regions in Southeast Asia: Trang Province in Thailand (7° 4’ N, 99° 47’ E), Lambir Hills National Park in Malaysia (4° 2’ N, 113° 50’ E), and central Kalimantan in Indonesia (0° 06’ S, 114° 0’ E). Because the study site in FRIM was not in the center of a 0.5° grid cell, we interpolated the data using four grid cells in the vicinity of the observation site. We used the weighted average according to the distance between each observation site and the center of each corresponding grid cell.Although the climate input data provided by ISI-MIP were already bias-corrected, we conducted additional bias correction at FRIM using a historical scenario for each GCM data set and the observed weather data from 1 January 1976 to 31 December 2004 based on previously presented protocol49. We did not implement any bias correction for the frequency of dry days or precipitation intensity of wet days49 because we only focused on the average precipitation.The variances in the annual fluctuation of the monthly mean precipitation were not the same between the observation data and historical GCM runs at FRIM. For all three GCMs (GFDL–ESM2M, IPSL–CM5A-LR, and MIROC5), the variances in the yearly fluctuation output by the GCMs tended to be larger than that of the observed data at the FRIM KEPONG weather station during winter and spring. On the other hand, during summer and fall, the variances output by the GCMs tended to be smaller than that of the observed data. These biases could not be corrected using the previous method49. Therefore, we conducted the following bias correction for these data:$${p}_{i,m,y}^{{{{{{rm{GCM}}}}}}* }={r}_{i,m,y}^{{{{{{rm{GCM}}}}}}}cdot left[{F}_{Gamma }^{-1}left({F}_{Gamma }left({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}|{k}_{m,y},{theta }_{m,y}right)|{k}_{m,y}^{* },{theta }_{m,y}^{* }right)cdot {rho }_{m,y}^{{{{{{rm{GCM}}}}}}}right],$$
(1)
where ({p}_{i,m,y}^{{{{{{rm{GCM}}}}}}* }) is the bias-corrected precipitation value of the target GCM at year y, month m, and date i. In the equation, ({r}_{i,m,y}^{{{{{{rm{GCM}}}}}}}) is the ratio of the precipitation value of the GCM relative to the monthly mean value. Then, the following equation is used:$${r}_{i,m,y}^{{{{{{rm{GCM}}}}}}}=frac{{p}_{i,m,y}^{{{{{{rm{GCM}}}}}}}}{{bar{p}}_{m,y}^{{{{{{rm{GCM}}}}}}}},$$
(2)
where ({p}_{i,m,y}^{{{{{{rm{GCM}}}}}}}) is the precipitation value (not bias-corrected) of the GCM at year (y), month (m), and date i and ({bar{p}}_{m,y}^{{{{{{rm{GCM}}}}}}}) is the monthly mean precipitation value of the GCM at year (y) and month (m). In Eq. 1, ({F}_{Gamma }) represents the cumulative distribution function of a gamma distribution, ({F}_{Gamma }^{-1}) represents the inverse function of the cumulative distribution function of the gamma distribution, and ({k}_{m,y}) and ({theta }_{m,y}) are the shape parameters. In Eq. 1, ({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}) indicates the deviation of the monthly mean from the normal climate value of the corresponding period, and this value is calculated as follows:$${delta }_{m,y}^{{{{{{rm{GCM}}}}}}}=frac{{bar{p}}_{m,y}^{{{{{{rm{GCM}}}}}}}}{{rho }_{m,y}^{{{{{{rm{GCM}}}}}}}},$$
(3)
where ({rho }_{m,y}^{{{{{{rm{GCM}}}}}}}) is the normal climate value during the target period. In this method, we defined the normal climate value as the mean of the monthly mean precipitation values over 31 years.$${rho }_{m,y}^{{{{{{rm{GCM}}}}}}}=frac{1}{31}mathop{sum }limits_{j=y-15}^{y+15}{bar{p}}_{m,j}^{{{{{{rm{GCM}}}}}}}.$$
(4)
When the mean of a gamma distribution is fixed at one, the shape parameters are represented as follows:$${k}_{m,y}=frac{1}{Vleft({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}right)},$$
(5)
$${theta }_{m,y}=frac{1}{{k}_{m,y}},$$
(6)
where (Vleft({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}right)) indicates the variance in ({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}) at month (m) over 31 years.In this method, we assumed that the ({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}) value follows a gamma distribution and that the ratio of the variance of ({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}) to the variance of ({delta }_{m,y}^{{{{{{rm{obs}}}}}}}) is maintained even in the future scenario. Here, ({delta }_{m,y}^{{{{{{rm{obs}}}}}}}) represents the deviation of the monthly mean in the observation data from the normal climate value.$${delta }_{m,y}^{{{{{{rm{obs}}}}}}}=frac{{bar{p}}_{m,y}^{{{{{{rm{obs}}}}}}}}{{rho }_{m}^{{{{{{rm{obs}}}}}}}},$$
(7)
$${rho }_{m}^{{{{{{rm{obs}}}}}}}=frac{1}{28}mathop{sum }limits_{j=1976}^{2004}{bar{p}}_{m,y}^{{{{{{rm{obs}}}}}}}.$$
(8)
In the above equations, ({bar{p}}_{m,y}^{{{{{{rm{obs}}}}}}}) indicates the monthly mean precipitation value in the observed data. As mentioned above, because we assume that the ratio of the variance in ({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}) to the variance in ({delta }_{m,y}^{{{{{{rm{obs}}}}}}}) is maintained, ({k}_{m,y}^{* }) and ({theta }_{m,y}^{* }) are calculated as follows:$${k}_{m,y}^{* }=frac{{k}_{m,y}}{alpha },$$
(9)
$${theta }_{m,y}^{* }=frac{1}{{k}_{m,y}^{* }},$$
(10)
where$$alpha =frac{Vleft({delta }_{m,y}^{{{{{{{rm{GCM}}}}}}}^{{{{{{rm{h}}}}}}}}right)}{Vleft({delta }_{m,y}^{{{{{{rm{obs}}}}}}}right)}.$$
(11)
In Eq. 11, ({delta }_{m,y}^{{{{{{{rm{GCM}}}}}}}^{{{{{{rm{h}}}}}}}}) is the deviation of the monthly mean of the historical GCM precipitation data from the normal climate value. Here, we defined the normal climate value as the average monthly mean during 1976–2004.The method proposed here is an original bias correction method, but the above equations are easily derived if we assume that the ({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}) value follows a gamma distribution and that the ratio of the variance in ({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}) to the variance in ({delta }_{m,y}^{{{{{{rm{obs}}}}}}}) is maintained even in the future scenario. Notably, because we combined this method with the bias correction method described previously49, Eq. 2 should be expressed as follows:$${r}_{i,m,y}^{{{{{{rm{GCM}}}}}}}=frac{{widetilde{p}}_{l,m,y}^{{{{{{rm{GCM}}}}}}}}{{bar{p}}_{m,y}^{{{{{{rm{GCM}}}}}}}},$$
(12)
where ({widetilde{p}}_{l,m,y}^{{{{{{rm{GCM}}}}}}}) is the precipitation data that are bias-corrected using the method described previously49. Bias-corrected data were compared with the data without bias correction (Supplementary Figs. 8–11).Statistical analyses and reproducibilityWe adopted previously presented models in which environmental triggers for floral induction accumulate for n1 days prior to the onset of floral induction21 (Supplementary Fig. 2). Flowers then develop for n2 days before opening (Supplementary Fig. 2). The model assumption of the time lag between floral induction and anthesis, which is denoted as n2, was validated by a previous finding in which the expression peaks of flowering-time genes, which are used as molecular markers of floral induction, were shown to occur at least one month before anthesis in Shorea curtisii19. S. curtissi is included in our data set. The CU at time t, ({{{{{rm{CU}}}}}}left(t|{theta }^{C}right)), is calculated as follows:$${{{{{rm{CU}}}}}}left(t|{theta }^{C}right)=mathop{sum }limits_{n={n}_{2}}^{{n}_{2}+{n}_{1}-1}{{{{{rm{max }}}}}}{bar{C}-xleft(t-nright),0},$$
(13)
where ({theta }^{C}=left{{n}_{1},{n}_{2},bar{C}right}) is the set of parameters and x(t) is the temperature at time t. Here, (bar{C}) indicates the threshold temperature. The term max{x1, x2} is a function that returns a larger value for the two arguments. Similarly, given ({theta }^{D}={{n}_{1},{n}_{2},bar{D}},) the DU at time t, ({{{{{rm{DU}}}}}}left(t|{theta }^{D}right)), is defined as the difference between the mean daily accumulation of rainfall over n1 days and a threshold rainfall level ((bar{D})):$${{{{{rm{DU}}}}}}left(t|{theta }^{D}right)={{{{{rm{max }}}}}}left{bar{D}-mathop{sum }limits_{n={n}_{2}}^{{n}_{2}+{n}_{1}-1}yleft(t-nright)/{n}_{1},0right},$$
(14)
where y(t) is the rainfall value at time t. The term max{x1, x2} is defined similarly as in Eq. 13.Logistic regression was performed using only the DU and using the product of CU and DU (CU × DU) as the explanatory variables and using the presence or absence of a first flowering event as the dependent variable for each phenological cluster. Because the number of phenological clusters is unknown, we performed forward selection on the cluster number based on the AIC. Let m be the number of phenological clusters based on the dendrogram drawn from the time-series clustering explained above (Supplementary Fig. 5). Given m phenological clusters, let ({G}_{k}^{m}) be the kth set of clusters in which the DU model is adopted for model fitting. Here, ({G}_{k}^{m}) indicates the set of cluster IDs, and k ranges from 0 to m(m+1)/2. For example, when m = 2 (i.e., there are two clusters, clusters 1 and 2), there are four cluster sets, calculated as follows:$${G}_{0}^{m=2}={},{G}_{1}^{m=2}={1},{G}_{2}^{m=2}={2},{G}_{3}^{m=2}={1,2},$$
(15)
where the element in the bracket indicates the ID of the cluster in which the DU model is adopted for model fitting. When k = 0, the DU model is not used; instead, the CU × DU model is adopted for model fitting for both clusters 1 and 2. Let i be the ith element of the vector E, which is defined as follows:$${{{{{bf{E}}}}}}={{t}_{1}^{1},,{t}_{2}^{1},…,,,{t}_{n}^{1},,…,,,{t}_{1}^{m},,{t}_{2}^{m},…,,{t}_{n}^{m}},$$
(16)
where n is the length of the time-series data for each cluster. Notably, n = 223 is the same for all species and clusters. The term ({t}_{1}^{m}) in the above equation denotes the first time point of the time series of length n for the species included in cluster m. Given m and k, let ({p}^{(m,k)}(i)) be the flowering probability of element i of vector E. The term ({p}^{(m,k)}(i)) is expressed as follows:$${{log }}left[frac{{p}^{left(m,kright)}left(iright)}{1-{p}^{left(m,kright)}left(iright)}right]= mathop{sum }limits_{j=1}^{m}{alpha }_{m,j}cdot {Z}_{m,j}left(iright)+mathop{sum }limits_{jin {G}_{k}^{left(mright)}}^{m}{beta }_{m,j}cdot {Z}_{m,j}left(iright)cdot {{{{{{rm{DU}}}}}}}_{m,j}left(i|{theta }_{j}^{D}right)\ +mathop{sum }limits_{jnotin {G}_{k}^{left(mright)}}^{m}{beta }_{m,j}cdot {Z}_{m,j}left(iright)cdot {{{{{rm{CU}}}}}}left(i|{theta }_{j}^{C}right)times {{{{{{rm{DU}}}}}}}_{m,j}left(i|{theta }_{j}^{D}right),$$
(17)
where ({Z}_{m,j}(i)) is the dummy variable indicating a cluster for i; ({Z}_{m,j}(i)) equals 1 if the ith element of E belongs to the jth cluster, otherwise it is zero, and ({alpha }_{m,j}) and ({beta }_{m,j}) in Eq. (5) are regression coefficients for the jth cluster when the species are grouped into m clusters. We estimate the parameters and the number of clusters based on a finite number of observations. Given the number of clusters m, for each of m clusters, the parameters were estimated by maximizing the loglikelihood value calculated for all combinations of potential parameter values for ({n}_{1},{n}_{2},bar{C},) and (bar{D}) within the ranges of [1 (min), 50 (max)] for n1, [1,50] for n2, [19,25] for (bar{C}), and [1,9] for (bar{D}). We varied the days (n1 and n2) by integers, temperature ((bar{C})) by tenths of a degree C, and daily precipitation ((bar{D})) by tenths of a mm. Regression coefficients (({alpha }_{m,j}), ({beta }_{m,j})) for all j values under a given m value and associated likelihoods were determined using generalized linear models with binomial error structures.With the results of the parameter estimations, we determined the number of clusters in two steps. For the first step, for a given m, we obtained (hat{k}(m)) according to the following equation:$$hat{k}(m)={arg }mathop{{min }}limits_{k}{{{{{{rm{AIC}}}}}}{m,k(m)},,k(m),=,0,,…,{2}^{m}}.$$
(18)
For the second step, with the results of (hat{k}) obtained from the first step, we obtained the estimate of the number of clusters according to forward selection by searching for the (hat{m}) value that satisfies the following inequalities:$${{{{{rm{AIC}}}}}}(hat{m},,hat{k}(hat{m})), < ,{{{{{rm{AIC}}}}}}(hat{m}+1,,hat{k}(hat{m}+1))cap {{{{{rm{AIC}}}}}}(hat{m},,hat{k}(hat{m})), < ,{{{{{rm{AIC}}}}}}(hat{m}-1,,hat{k}(hat{m}-1)).$$ (19) For model fitting, the first flowering month was extracted from the flowering phenology data. When flowering lasted more than 1 month, the month after the first flowering month was replaced by a value of zero (absence of flowering). If the month before the first flowering month was a missing value, the first flowering month was treated as a missing value and was not used for further analyses. We assumed that phenology monitoring was performed on the first date of each month.Projections of 21st-century changes in flowering phenologyWe used two scenarios (RCP2.6 and RCP8.5) to forecast future reproductive phenology in dipterocarp species for each of the three GCMs (GFDL–ESM2M, IPSL–CM5A-LR, and MIROC5). We predicted the flowering probability per month for each phenological cluster during the periods from 1 May 1976–31 March 1996 and from 1 January 2050–31 December 2099 based on the best model (Supplementary Table 2). The predicted flowering probability during the 2050–2099 period was normalized to that during the 1976–1996 period for each climate scenario and for each of three GCMs. To compare the seasonal patterns between 1976–1996 and 2050–2099, the predicted flowering probability was averaged for each month from January to December and plotted for each month in Fig. 6. R version 3.6.3 was used for all analyses.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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