Temporal Trends in Biodiversity Intactness Vary with Baseline Levels Across Regions and Climates

Abstract

Exploring the relationship between the magnitude and temporal trend of the Biodiversity Intactness Index (BII) is critical to assessing current and future changes in biodiversity intactness. However, investigations into the relationship between BII magnitude and trends worldwide have been limited. Using annual BII time series data from 2000 to 2020, we assess the global spatial patterns of BII magnitude and trend, as well as their relationship. Our findings reveal four key insights: First, the global mean BII magnitude exhibits pronounced latitudinal and climatic heterogeneity, with higher values observed in less human-impacted regions. Second, biodiversity trends display contrasting trajectories between areas of differing baseline intactness—regions with initially low biodiversity (BII < 0.50) show recovery potential (−0.007 ± 0.021 decade⁻¹), while high-biodiversity areas (BII > 0.90) face accelerated declines (0.002 ± 0.012 decade⁻¹). Third, continental and climatic disparities are striking: Europe and temperate zones demonstrate stabilizing trends, whereas tropical and polar regions experience marked deterioration. Fourth, climate variables, particularly precipitation seasonality (BIO15) and mean temperature of the coldest quarter (BIO11), show strong negative correlations with the BII trend, indicating climate-linked declines while exhibiting minimal influence on baseline BII magnitude. This study has the potential to help develop more efficient sustainable practices and behaviors to mitigate biodiversity disparities and achieve sustainable development goals.

1. Introduction

Biodiversity encompasses the variability of life across species, ecosystems, and genetic diversity [1,2]. It underpins sustainable human development [3,4,5,6,7] through ecosystem services, cultural significance, and socio-economic value [8,9]. The Convention on Biological Diversity (CBD) is the first global agreement aimed at biodiversity conservation and sustainability. At its 15th Conference of the Parties (COP15), the CBD specified the need for “urgent and integrated action” to protect biodiversity. The final implementation of biodiversity conservation and management decisions has become an essential concern for the world [10].

The biodiversity intactness index (BII) is defined as the mean abundance of a broad and diverse set of species in an area relative to their reference populations [11,12]. For example, a BII of 50% would indicate that the abundance of species in a specific area has been reduced by half compared to pristine conditions [13]. The abundance-based BII (termed as BII hereafter) is a simple, robust indicator of biodiversity that adequately meets the criteria set by the CBD and has been proposed as the most widely used indicator to express biodiversity [12,14,15]. Numerous studies have therefore used BII to investigate the temporal patterns in terms of magnitude and temporal trend of biodiversity at regional or global scales [11,13,16,17,18,19,20]. The results of these studies have indicated that the magnitude and temporal trend of biodiversity are characterized by large spatial heterogeneity. For instance, Newbold et al. [21] investigated the BII of the world’s land surface in 2015 and found that the global mean magnitude of BII is estimated to be 0.85, but large geographic differences are also observed. Relatively high values are observed throughout much of the high Northern latitude areas and are predicted to have a value of more than 0.90 (the ‘safe operating space’ of the Planetary Boundaries framework proposed by Steffen et al. [22].

However, low levels (estimated to be around 0.70) are seen in low and middle-latitude areas, such as much of Western and Central Asia, Southern Africa, and Oceania. Subsequently, Hill et al. [18] show the mean BII at 0.79, which is somewhat smaller than the BII value estimated by Newbold et al. [21], probably due to the difference in the modeling approach [18,23]. Despite differences in the models and global estimates of the mean BII, the two studies all highlight the geographical heterogeneity of BII, which is likely related to the characteristics (e.g., environmental features, accessibility, size, and sensitivity) of the ecoregion [19].

Many studies have also explored temporal trends of biodiversity and have observed a gradual decrease in biodiversity trends due to various natural and anthropogenic processes [24,25,26], with the trend ranging from −0.3% to −0.05% per decade [27,28,29]. However, there are also instances where local biodiversity shows an insignificant change over time [30]. The magnitude and direction of these trends vary widely due to geographical differences in natural and anthropogenic pressures. For example, while most African regions show declines in the BII trend, some Asian and European regions show an overall improvement in biodiversity [20]. Moreover, biodiversity losses (i.e., decreasing biodiversity trend) tend to occur much in highly biodiverse areas [20], suggesting a contrasting difference in biodiversity trend between different biodiversity magnitudes.

Although progress has been made, there is still a lack of comprehensive investigation into the magnitude and temporal trend of global biodiversity. The two indicators measure distinct and complementary components of biodiversity and are essential in understanding the health of the ecoregion. However, it remains unclear how biodiversity levels relate to global biodiversity trends, which is crucial for understanding the impacts of global change on past and future biodiversity. In this study, we conduct a statistical analysis of annual BII time series data from 2000 to 2020 and examine the mean magnitude and temporal trend of biodiversity intactness at multiple scales. In addition, we also explored the relationship between BII magnitude and trend and bioclimatic variables. In this article, BII magnitude refers to the magnitude of change in biodiversity, hereinafter referred to as BII magnitude. Our study aims to answer two main questions: (1) What are the spatial patterns of global biodiversity magnitude and trend? (2) How is the global biodiversity trend related to its magnitude and its relationship with bioclimatic variables? We believe our study has the potential to deepen our understanding of the current state of biodiversity and how it may change in the future.

2. Materials and Methods

2.1. Study Area

Our study area consisted of the global countries that incorporate measures of local biodiversity, as identified by the BII data from Projecting Responses of Ecological Diversity in Changing Terrestrial Systems (PREDICTS) (refer to Section 2.2 for more information). In total, 201 countries were selected based on this criterion (refer to Figure 1). The selected countries of this study are situated across six continents: Asia, Europe, Africa, North America, South America, and Oceania. They are distributed into five main climate groups: tropical (group A), arid (group B), temperate (group C), D continental (group D), and polar (group E) zones, according to the updated Köppen–Geiger classification scheme [31] (refer to Section 2.2).

2.2. Dataset

The study utilized several sources of data, including the BII data from PREDICTS, global country boundary data, updated Köppen–Geiger classification map, and auxiliary data.

The global PREDICTS BII data is publicly accessible at the Natural History Museum Data Portal (https://data.nhm.ac.uk/dataset/bii-bte, accessed on 15 January 2024) [32]. PREDICTS focuses on the sensitivity of biodiversity variations to land use and associated pressure [32,33], as these remain the predominant forces affecting biodiversity on Earth [34,35,36]. To the best of our knowledge, the PREDICTS BII is the largest and most taxonomically extensive global database of biodiversity measures compiled to date [32], and it has been widely utilized in various studies [12,13,18]. The BII is between 0 and 1.0, and a larger BII value indicates higher biodiversity intactness [21]. The PREDICTS offers a summary of the annual BII time series data provided at the country level, with a decade-long interval before the year 2000 (1970, 1980, and 1990) and a yearly interval from 2000 to 2050. The PREDICTS BII can be grouped into two categories: historical data, which was calculated based on local biodiversity measures obtained from thousands of sites globally before 2014, and simulated data, which was modeled under five Shared Socio-economic Pathways from 2015 to 2050. It is important to note that this study used annual BII time series data from 2000 to 2020, consisting of historical (2000–2014) and simulated (2015–2020) BII data. Therefore, the study examined the temporal trend of global BII between 2000 and 2020.

The utilization of the BII time series from 2000 to 2020 was based on a trade-off between reducing potential uncertainties associated with simulated data and accurately identifying temporal trends in the available BII data. This time period was also selected because it corresponds to the period (2000–2020) of significant biodiversity loss due to both natural and anthropogenic pressures [24,35], particularly in developing regions such as Africa, South America, and Southeast Asia where biodiversity intactness is usually high [13]. A closer discussion of the potential uncertainties associated with the used time series BII data from 2000 to 2020 is presented in Section 4.1.

The administrative boundary dataset used in the study provides the boundary of all the countries and continents globally and is publicly available from the Natural Earth (http://www.naturalearthdata.com/downloads, accessed on 26 January 2024). Natural Earth is well-known as a public information-sharing platform created by many volunteers and supported by the North American Cartographic Information Society (NACIS).

The widely used updated Köppen–Geiger climate classification map is also publicly available at the Climate Change and Infectious Diseases Group [31] (https://koeppen-geiger.vu-wien.ac.at/, accessed on 9 April 2024). The updated Köppen–Geiger climate classification map is created with a spatial resolution of 5 arc minutes (appropriately 5 km). It classifies the world’s climates into five main categories, including tropical (group A), arid (group B), temperate (group C), D continental (group D), and polar (group E) climate zones, based on the patterns of seasonal precipitation and temperature.

Furthermore, to investigate the influence of climate on BII, monthly climate data from 2000 to 2020 (resolution: 2.5 arc-minutes) [37,38] were acquired, encompassing minimum temperature (°C), maximum temperature (°C), and total precipitation (mm) (https://www.worldclim.org/data/monthlywth.html, accessed on 11 February 2025). This data was processed using the ‘biovars’ function from the R package dismo to generate a total of nineteen bioclimatic variables (BIO1–BIO19) [39,40,41]. The description of bioclimatic variables is shown in

Table 1. Bioclimatic variables.

Abbreviation	Description	Unit
BIO 01	Mean annual air temperature	°C
BIO 02	Mean diurnal range	°C
BIO 03	Isothermality	°C
BIO 04	Temperature seasonality	°C
BIO 05	Max temperature of warmest month	°C
BIO 06	Min temperature of coldest month	°C
BIO 07	Temperature annual range	°C
BIO 08	Mean Temperature of wettest quarter	°C
BIO 09	Mean temperature of driest quarter	°C
BIO 10	Mean temperature of warmest quarter	°C
BIO 11	Mean temperature of coldest quarter	°C
BIO 12	Annual Precipitation amount	°C
BIO 13	Precipitation amount of the wettest Month	mm
BIO 14	Precipitation amount of the driest month	mm
BIO 15	Precipitation seasonality	mm
BIO 16	Precipitation of wettest quarter	mm
BIO 17	Precipitation of driest quarter	mm
BIO 18	Precipitation of warmest quarter	mm
BIO 19	Precipitation of coldest quarter	mm

.

2.3. Analysis of Global Biodiversity Magnitude and Trend

The analytical workflow is summarized in Figure 2. Given that the BII has been proposed as an indicator of global ecosystem health [22], this study comprehensively analyzed the mean magnitude and trend of the BII and investigated the relationship between the two. For each country, we calculated the mean magnitude by averaging the annual BII time series from 2000 to 2020. To determine the temporal trend, we used the Mann–Kendall trend test and Sen slope estimation in R (Version 4.4.0) [42,43], which are robust methods for detecting trends and are recommended by the World Meteorological Organization [44]. The calculation formulas for the Mann–Kendall trend test and Sen slope estimation are shown in Section 2.3.1 and Section 2.3.2. A negative trend of the BII indicates a reduction in biodiversity intactness from 2000 to 2020, while a positive BII trend indicates an enhancement of biodiversity intactness.

We then aggregated country-level statistics at the regional (e.g., country clusters, continents, and climate zones) and global scales. It should be noted that the statistics of country clusters were computed as the average of the 20 country-level statistics according to ascending order in BII magnitude. Additionally, Pearson correlation analysis was employed to examine the relationship between the mean magnitude and trend of BII, as well as the association between both of these and the 19 bioclimatic variables.

2.3.1. Mann–Kendall Test

The Mann–Kendall [42,45] is calculated as:

$$S={\sum }_{i=1}^{n-1}{\sum }_{j=i+1}^n\mathrm{sgn}\left(x_j-x_i\right)$$

(1)

where n is the number of data points, x_i and x_j are the data values in the time series i and j (j > i), respectively, and sgn (x_j − x_i) is the sign function as:

$${\mathrm{s}\mathrm{g}\mathrm{n}}_{(x_j-x_i)}=\left\{\begin{array}{c}+1 if x_j-x_i>0\\ 0 if x_j-x_i=0\\ -1 if x_j-x_i<0\end{array}\right.$$

(2)

The variance is computed as:

$$Var\left(S\right)={\displaystyle \frac{n\left(n-1\right)\left(2n+5\right)-{\sum }_{i=1}^P{t}_i(t_i-1)(2t_i+5)}{18}}$$

(3)

where n is the number of data points, P is the number of tied groups in the data, the summary sign (P) indicates the summation over all tied groups, and t_i is the number of data values in the P_th group. If there are no tied groups, this summary process can be ignored. A tied group is a set of sample data having the same value. In cases where the sample size n > 30, the standard normal test statistic Z_S is computed using Equation (4):

$$Z_S=\left\{\begin{array}{c}{\displaystyle \frac{S-1}{\sqrt{Var\left(S\right)}}} if S>0\\ 0 if S=0\\ {\displaystyle \frac{S+1}{\sqrt{Var\left(S\right)}}} if S<0\end{array}\right.$$

(4)

Positive values of Z_S indicate increasing trends, while negative Z_S values show decreasing trends. Testing trends is done at the specific significance level. When |Z_S| > Z_1−α/2, the null hypothesis is rejected, and a significant trend exists in the time series. Z_1−α/2 is obtained from the standard normal distribution table. At the 5% significance level, the null hypothesis of no trend is rejected if |Z_S| > 1.96 and rejected if |Z_S| > 2.576 at the 1% significance level.

2.3.2. Sen’s Slope

Sen’s slope estimator [43] provides a robust estimate of the magnitude of the trend and is calculated as the median of the slopes between all pairs of points in the time series:

$$SS=Median[{(X}_j-X_i)/(j-i)]$$

(5)

where X_i and X_j are the data values at times i_th and j_th, respectively (with j > i). A positive value of SS indicates an increasing trend, while a negative value indicates a decreasing trend.

3. Results

3.1. Spatial Pattern of Biodiversity Magnitude

At the global scale, the average magnitude of the BII is 0.76 ± 0.16 (mean ± one standard deviation) between 2000 and 2020 (Figure 3a). Our global estimate of mean BII magnitude is roughly equal to the 0.78 estimated by [18], whereas it is somewhat smaller than the 0.85 estimated by Newbold et al. [21], probably due to differences in modeling approaches. Globally, 75% of countries exhibit baseline BII values below the safe threshold of 0.90 [22]. Notably, 6% of countries fall below 0.50, indicating severe biodiversity degradation (Figure 3b). A small portion (25%) of the countries, mostly located in northern Africa and South America (Figure 3a), have a value of BII magnitude larger than the safe limit of 0.90. Countries with a relatively low BII (BII < 0.50) are scattered around the world.

At the regional level, the BII magnitude reveals a large latitudinal variability (Figure 3c). The BII magnitude tends to be higher at 60 degrees south and north than at other latitudes, with BII magnitudes of 0.95 ± 0.07 and 0.87 ± 0.12 at 60 degrees south and north, respectively, probably because these high latitudes are less affected by human influence. In comparison, the magnitude of the BII shows less latitudinal variability between 50 degrees south and 50 degrees north, characterized by a larger BII magnitude in the middle and high latitudes and a smaller BII magnitude in the low and middle latitudes (Figure 3c). The magnitude of the BII increases with latitude from 50 to 20 degrees south and from 50 to 20 degrees north, with the BII magnitude ranging from 0.60 ± 0.00 to 0.80 ± 0.12 between 50 to 20 degrees south and from 0.64 ± 0.12 to 0.85 ± 0.17 between 50 to 20 degrees north. In contrast, the magnitude of the BII decreases with latitude from 20 to 0 degrees south and from 20 to 0 degrees north, with the BII magnitude ranging from 0.80 ± 0.12 to 0.74 ± 0.16 between 20 to 0 degrees south and from 0.85 ± 0.17 to 0.74 ± 0.16 between 20 to 0 degrees north (Figure 3c).

There is a large variation in the BII magnitude across different continents and climate zones (Figure 4a–d). Continental BII magnitudes ranged from approximately 0.69 (Europe, North America) to 0.80 (South America), all below the proposed 0.90 safe limit (Figure 4a). The BII magnitude in Europe and North America is much lower than the proposed safe limit of 0.90 [22], which highlights the need for caution in local biodiversity conservation efforts. The variation in BII magnitude across continents may be due to differences in the structure and function of the ecoregion in response to human development levels [19]. South America has the highest percentage of high BII magnitude (BII > 0.90) (28.57%), followed by North America (22.22%), Asia (19.51%), and Africa (13.73%). The percentages of high BII magnitude are both lower in Europe (9.30%) and Oceania (0.00%) than in other continents. The percentage of high BII magnitude in Oceania is 0, indicating that all countries in Oceania have a BII magnitude lower than the proposed safe limit of 0.90. The percentage of low BII magnitude (BII < 0.50) is highest in Oceania (33.33%), followed by North America (16.67%), South America (14.29%), Asia (9.75%) and Europe (9.30%). The percentage of high BII is lowest in Africa (5.88%) (Figure 4c).

By climate zone, the polar zone has the highest BII (0.84 ± 0.06), and the arid zone comes in second (0.82 ± 0.13), followed by the continental (0.77 ± 0.14), tropical (0.74 ± 0.17), temperate (0.68 ± 0.15) zones (Figure 4b). This variation in BII across climate zones echoes the impacts of background climate (e.g., temperature and precipitation) on biodiversity [46,47,48,49,50]. The arid zone has the highest percentage of high BII magnitude (BII > 0.90) (30.23%). The percentage of high BII magnitude is higher in the tropical zone (27.42%) than in the polar zone (25.00%) and in the continental zone (22.73%). The percentage of high BII is lowest in the temperate zone (7.84%) (Figure 4d). The percentage of low BII magnitude (BII < 0.50) is higher in the temperate zone (11.76%) than in the tropical zone (8.06%) and in the continental zone (4.55%). All countries in arid and polar zones have a BII magnitude lower than 0.50, characterized by the percentage of high BII magnitude in arid and polar zones of 0.00 (Figure 4d).

The magnitude of the BII varied substantially between countries, spanning a range of nearly 0.7 units (Figure 5). A subset of countries, primarily in densely populated or heavily modified regions (e.g., Singapore, Bangladesh, UK), exhibited very low BII magnitudes (all < 0.50). Conversely, another group, predominantly located in arid regions of the Middle East (e.g., Iraq, UAE, Qatar), consistently showed the highest possible BII magnitude (1.00), indicating minimal human impact relative to the baseline in those specific areas. These countries are mostly in Asia, particularly in the Middle East.

3.2. Spatial Pattern of Biodiversity Trend

Globally, BII shows a marginal declining trend over the past two decades (Figure 6a), consistent with prior estimates [27,28,29]. A substantial decreasing trend of the BII is detected for 56% of global countries, with the mean BII trend of −0.014 ± 0.017 decade⁻¹ (Figure 6b). There are 44% of global countries, mainly in Europe and Mesoamerica, showing an increasing trend of the BII with the mean trend of 0.012 ± 0.014 decade⁻¹. This increase in biodiversity intactness may be due to improvements in biodiversity conservation [20,51].

On a regional scale, the BII trend varies with latitude (Figure 6c). The BII trend is relatively higher from 60 to 50 degrees south and from 60 to 40 degrees north than at other latitudes, with BII trends ranging from 0.01 to 0.02 decade⁻¹ (Figure 6c). The positive BII trend is also observed at 30 degrees south, with a BII trend of 0.01 ± 0.01 decade⁻¹. The BII trend is negative at 40 degrees south and from 30 degrees south to 30 degrees north. There is a maximum negative BII trend at 40 degrees, with a BII trend of −0.02 ± 0.02 decade⁻¹. The trend of the BII tends to decrease with latitude from 30 to 0 degrees south and from 30 to 0 degrees north, with the BII trend ranging from −0.02 ± 0.03 to −0.02 ± 0.02 between 30 to 0 degrees south and from 0.85 ± 0.17 to −0.01 ± 0.01 between 30 to 0 degrees north (Figure 6c).

The variation in biodiversity magnitude and trend across continents and climate zones is shown in Figure 7a–d. Our results also show large disparities in the biodiversity trend across continents, with Europe (0.009 ± 0.017 decade⁻¹) having the largest BII trend, followed by Oceania (0.008 ± 0.002 decade⁻¹), North America (−0.003 ± 0.008 decade⁻¹), Asia (−0.004 ± 0.020 decade⁻¹), South America (−0.008 ± 0.010 decade⁻¹), and Africa (−0.014 ± 0.023 decade⁻¹) (Figure 7a). This discrepancy may be related to the interaction of natural and anthropogenic factors, such as human development and land-use management [51,52,53]. The percentage of positive BII trend (BII trend > 0) is highest in Europe (69.77%) and Oceania (66.67%), followed by Asia (46.34%), North America (33.33%), and Africa (25.49%). The percentage of positive BII trend is lowest in South America (14.28%) (Figure 7c). South America has the highest percentage of negative BII trend (BII trend < 0) (85.71%), followed by Africa (74.51%), North America (66.67%), and Asia (53.65%). The percentages of negative BII trends are lower in Oceania (33.33%) and Europe (30.23%) than in other continents.

At the climate zone level, significant declining trends characterized the tropical zone, while milder declines or relative stability were observed in the arid and polar zones. In contrast, the continental and temperate zones exhibited slightly increasing trends over the past two decades (Figure 7b). The continental zone has the highest percentage of positive BII trend (77.27%). The percentage of positive BII trend is higher in the temperate zone (49.02%) than in the arid zone (41.86%) and in the tropical zone (27.42%). The percentage of positive BII trend (BII trend > 0) is lowest in the polar zone (25.00%) (Figure 7d). In the polar zone (75.00%) and the tropical zone (72.58%), the percentage of positive BII trends is over 70%. The percentage of negative BII trends (BII trend < 0) is relatively higher in the tropical zone (58.14%) and the temperate zone (50.98%), which is more than the percentage of negative BII trends in the continental zone (22.73%).

There is a significant difference in the BII trend between countries, characterized by a difference of about 0.17 decade⁻¹ between the maximum BII trend (BII trend = 0.066 decade⁻¹) and the minimum BII trend (BII = −0.11 decade⁻¹). Across all 201 countries, the top 10 countries with the highest positive BII trends are Netherlands (0.030 decade⁻¹), Italy (0.031 decade⁻¹), Trinidad and Tobago (0.032 decade⁻¹), Spain (0.033 decade⁻¹), Liechtenstein (0.034 decade⁻¹), Hungary (0.047 decade⁻¹), San Marino (0.047 decade⁻¹), Poland (0.054 decade⁻¹), Georgia (0.057 decade⁻¹), and Samoa (0.066 decade⁻¹). These countries are mainly located in Europe, which accounts for 70% of these ten countries. The top 10 countries with the highest negative BII trends are Sierra Leone (−0.11 decade⁻¹), Lebanon (−0.065 decade⁻¹), Comoros (−0.060 decade⁻¹), Tanzania (−0.050 decade⁻¹), Togo (−0.049 decade⁻¹), Nigeria (−0.044 decade⁻¹), Burundi (−0.042 decade⁻¹), Thailand (−0.040 decade⁻¹), Benin (−0.038 decade⁻¹), and Malawi (−0.037 decade⁻¹) (Figure 8). These countries are spread across Africa (accounting for 80% of these ten countries) and Asia (20%).

3.3. Spatial Pattern of BII Ratio Between Bii Trend and BII Magnitude

The link between the magnitude and trend of BII is presented in Figure 9. At a holistic level, we find that the mean BII magnitude in countries with a negative BII trend is approximately similar to that in countries with a positive BII trend (0.77 ± 0.16 vs. 0.74 ± 0.16) (Figure 9a). The average ratio between the BII trend and BII magnitude across the world is −0.003 ± 0.020 (Figure 9b). A relatively small ratio, with a value of less than −0.003, is recorded for 40% of the countries, with significant values in Central Africa and South-East Asia. In comparison, a relatively large ratio, with a value greater than −0.003, is detected for 60% of the countries, characterized by profound values in most parts of Europe (Figure 9b).

The ratio between BII magnitude and trend also varies dramatically around the world, accompanied by the highly diverse ratio between continents and climate zones (Figure 10). A large BII ratio may imply one of the following two scenarios: (a) a large BII trend, (b) a small BII magnitude, or (c) both a large BII trend and a small BII magnitude. Our results indicate that Europe has the highest BII ratio (0.015 ± 0.027), as it simultaneously has the highest BII trend (0.009 ± 0.017 decade⁻¹) and the lowest BII magnitude (0.69 ± 0.14) (Figure 10a). Oceania is shown to have a relatively higher BII ratio (0.010 ± 0.001), which is associated with a relatively higher BII trend (0.008 ± 0.002 decade⁻¹), although it also has a large BII magnitude (0.78 ± 0.12). The BII ratio in North America comes in third (−0.004 ± 0.013), which is attributed to a relatively small BII magnitude (0.69 ± 0.14). We observe a lower BII ratio of −0.005 ± 0.033, −0.021 ± 0.037 and−0.012 ± 0.018 in Asia, South America, and Africa than in other continents, which is a result of the relatively small BII trend (−0.0075 ± 0.010 decade⁻¹, −0.0035 ± 0.020 decade⁻¹, −0.014 ± 0.023 decade⁻¹ in South America, Asia and Africa, respectively) and the large BII magnitude is (0.80 ± 0.17, 0.76 ± 0.16 and 0.75 ± 0.14). By climate zone, the relationship between BII magnitude and trend across different climate zones follows a similar pattern to that observed between different continents. The temperate and continental zones have a larger BII ratio (0.006 ± 0.038 and 0.0037 ± 0.013), which is due to a combined relatively large BII trend (0.002 ± 0.01 decade⁻¹, and 0.004 ± 0.023 decade⁻¹) comparatively low BII magnitude (0.68 ± 0.15, and 0.77 ± 0.14) in these two climate zones (Figure 10b). In comparison, arid, polar and tropical zones show a lower BII ratio (−0.003 ± 0.016, −0.003 ± 0.005, and −0.018 ± 0.036); this is mainly because of a relatively small BII trend (−0.003 ± 0.011 decade⁻¹, −0.003 ± 0.005 and −0.012 ± 0.022) and a large BII magnitude (0.82 ± 0.13, 0.84 ± 0.06 and 0.74 ± 0.17).

There is a large variation in the BII trend (or BII magnitude) under different values of BII magnitude (or BII trend). From the perspective of BII magnitude, the mean BII trend tends to be negative (or positive), provided that the BII magnitude is less than (or more than) 0.90. Specifically, the BII trend generally increases with BII magnitude in two phases, including the first phase with BII magnitude less than 0.70 and the second phase with BII magnitude greater than 0.70 (Figure 11a). The mean BII trend increases from −0.007 ± 0.021 decade⁻¹ to −0.001 ± 0.020 decade⁻¹ for the first phase and increases from −0.006 ± 0.014 decade⁻¹ to 0.002 ± 0.012 decade⁻¹ for the second phase. From the perspective of the BII trend, the BII magnitude is generally characterized by a concave upward curve along with the BII trend, with the BII magnitude peaking at the BII trend of −0.002 to 0.00 decade⁻¹ (Figure 11b). The mean BII magnitude increases from 0.61 ± 0.11 to 0.80 ± 0.16 for the phase of negative BII trend. While the mean BII magnitude generally decreases from 0.76 ± 0.16 to 0.70 ± 0.15 for the phase of positive BII trend.

3.4. Relationship of BII Magnitude and BII Trend to Bioclimatic Variables

Based on the correlation analysis between bioclimatic variables (BIO1–BIO19) and BII trend and BII magnitude, three key patterns emerge (Figure 12). First, for BII Magnitude, correlations are generally weak and non-significant (|r| < 0.15, p > 0.05), except for BIO9 (Mean Temperature of Driest Quarter, r = 0.14, p < 0.05) and BIO10 (Mean Temperature of Warmest Quarter, r = 0.14, p < 0.05) (Figure 12a), indicating minimal climate-driven influence on baseline biodiversity. Second, the BII trend shows strong negative correlations with multiple variables, highlighting climate-linked declines. BIO15 (Precipitation Seasonality, r = −0.33, p < 0.001) and BIO11 (Mean Temperature of Coldest Quarter, r = −0.31, p < 0.001) (Figure 12b) are most significant, suggesting reduced biodiversity resilience under increased temperature/precipitation variability. Third, BIO4 (Temperature Seasonality) paradoxically associates with BII Trend (r = 0.28, p < 0.001), possibly reflecting complex seasonal interactions. Overall, climate variables more strongly drive BII trends than BII magnitude, with BIO11 and BIO15 as critical stressors.

Based on the correlation analysis (Figure 12), spatial patterns were generated using BIO10 and BIO15—the bioclimatic variables exhibiting the strongest correlations with BII magnitude and BII trend, respectively—as presented in Figure 13. BIO10 ranged from −12.94 °C to 39.30 °C with a mean of 19.43 °C (Figure 13a). Elevated BIO10 values predominantly occurred between 30 °S and 30 °N, coinciding with the latitudinal belt where most countries exhibited BII magnitude > 0.80. BIO15 averaged 61.86 mm with a maximum of 234.48 mm (Figure 13b). BIO15 values were obviously higher in tropical and arid regions compared to other areas, indicating pronounced precipitation seasonality. Within these regions, countries predominantly exhibited negative BII trends. Conversely, countries in areas with lower BIO15 values (e.g., Europe) demonstrated predominantly positive BII trends.

4. Discussion

4.1. Unevenness of BII Characteristics Across Continents and Climates

We use annual BII time series data from 2000 to 2020 to investigate global spatial patterns of BII magnitude and trend and their relationship. We find that the BII features with different geographical contexts and climatic gradients over the last two decades show dramatic unevenness. The first is the unevenness of BII features between different continents. The BII magnitude is highest in South America and the lowest in Europe compared to other continents (Figure 4a). In comparison, the BII trend demonstrates the greatest value in Europe (0.009 ± 0.017 decade⁻¹) and the smallest value in Africa (−0.014 ± 0.023 decade⁻¹) relative to other continents (Figure 7a). The second is the unevenness of BII features between different climates. The polar zone has the highest BII (0.84 ± 0.06), while the temperate zone has the lowest BII (0.68 ± 0.15) relative to other climates (Figure 4b). The largest and smallest BII trends are observed in the temperate zone (0.002 ± 0.01 decade⁻¹) and the tropical zone (−0.012 ± 0.022 decade⁻¹) (Figure 7b). The unevenness of BII features across continents and climates echoes the influence of natural and anthropogenic pressures (e.g., temperature and precipitation) on biodiversity [19,47,50].

4.2. Unevenness Between the BII Magnitude and BII Trend

At the global level, the BII trend is positive but statistically insignificant (r = 0.11, p > 0.05) correlated with the BII magnitude (Figure 14a). However, we identify a positive and significant linear relationship (r = 0.38, p < 0.05) between BII magnitude and BII trend for the country classifications (the country classification statistics are calculated as the average of the 20 country-level statistics in ascending order of BII magnitude) (Figure 14b). The further aggregated result shows that the mean BII trend generally increases with BII magnitude in the phase with BII magnitude less than 0.70 and in the phase with BII magnitude greater than 0.70 (Figure 11a). However, at the regional scale, a larger (or smaller) BII trend appears to occur in low (or high) biodiverse areas. For example, Africa and Southeast Asia, where the magnitude of BII is typically high, show a substantial declining trend in BII [13], while the opposite is observed for much of Europe (Figure 10a). The discrepancy in the relationship between the BII magnitude and BII trend at different statistical scales may be due to large differences in natural and anthropogenic processes between different countries or regions [20,51].

On the one hand, individual countries with their own biogeographical characteristics may show patterns that differ from the regional pattern derived from the spatial averaging process. On the other hand, global aggregation partially neutralizes positive and negative differences in the regional pattern. The spatial averaging process suppresses the individuality of each country’s record and unravels the generality, but it deserves serious concern to avoid holistic neutralization caused by over-aggregation. These findings raise the alarm that an inappropriate choice of statistical scale is likely to lead to an incomplete interpretation of the relationship between BII magnitude and BII trend. Consequently, conclusions regarding the relationship between BII magnitude and BII trend for a specific scale should appropriately be made in the context of that specific scale. Although there is disagreement about the relationship between BII magnitude and BII trend on various statistical scales, these investigations all show a contrasting difference in BII trend between different BII magnitude levels. These findings would be useful for future biodiversity conservation and sustainability management (see implications in Section 4.3).

4.3. Limitations and Future Research

Some uncertainties still exist, and further efforts are required. First, our study focused on the dynamics of BII across global countries in the past 20 years, during which natural and anthropogenic processes have posed a serious threat to terrestrial biodiversity [51,52,53]. Our global estimate of mean BII magnitude (at 0.76 ± 0.16) is somewhat smaller than the 0.85 estimated by Newbold et al. [21], and the mean BII trend (at −0.003 ± 0.019 decade⁻¹) is slightly lower than the previously reported biodiversity trend ranging −0.003 to −0.0005 per decade [20,27,29]. These differences may be due to variations in the length of time series and spatial samplings [20,54]. Although there are slight numerical differences in the estimated mean BII magnitude and trend between our results and previous studies, they all show a relatively lower mean BII as compared to the proposed safe limit of 0.90 [22] and the overall decreasing trend in BII across global countries over the past two decades.

Future works may incorporate more historical data to extend the length of the study period and the number of spatial samplings, thereby assessing the biodiversity dynamics better. Second, we have explored the spatial pattern of biodiversity dynamics from a large-scale perspective to identify general laws for the relationship between BII magnitude and trend. Our analysis intended to complement, rather than replace, local-specific studies such as those conducted at city and site scales, which are essential to designing local-scale biodiversity conservation strategies. For example, urban environments are recognized as “harbingers” of future global climate change [55,56,57] and have multiple effects on biodiversity and ecosystem service. Identifying the impacts of urban environments on biodiversity and the underlying mechanisms can help design local-scale optimization strategies of biodiversity conservation and improve our understanding of biodiversity responses to global environmental change [58]. Third, we have used the widely accepted and globally acquirable BII to assess biodiversity dynamics. However, the BII can be considered complementary to other measures of biodiversity. Ideally, it should be used in conjunction with those aimed at the species level, such as the STAR metric derived from the IUCN Red List [58,59] and range rarity metrics [17], as both of these metrics measure the importance and threat of species across the world. Fourth, it is expected that change in BII will alter the potentiality of ecosystems to deliver services, but such a relationship is likely to be complex. Ecosystem services may need different spatial patterns of BII. For example, some may necessitate high BII levels but only in specific and discrete areas. In contrast, others may require a more widespread area to contribute to the service and show less sensitivity to the BII magnitude.

Understanding the locations where functioning is at risk or may exceed tipping points and how these contribute to planetary boundaries is essential when targeting restoration actions [19]. Further studies are needed to link BII with the risk of ecosystem services and the physical mechanism behind their relationship so as to guide management actions. Fifth, our study relies on the PREDICTS database, which aggregates biodiversity indicators at the national level. While this approach captures broad trends, it may inadequately represent regions with pronounced microclimatic variability, such as the Carpathian Basin. For example, Hungary’s apparent BII recovery (+0.047 decade⁻¹) conflicts with localized studies documenting biodiversity stresses from compounded heatwaves and agricultural intensification [60,61]. These discrepancies suggest that our continental-scale analysis might overgeneralize recovery signals in areas undergoing complex socio-ecological transitions. We recommend caution when interpreting temperate European trends and advocate for nested modeling frameworks that couple global BII projections with regional climate-biodiversity feedback.

Despite the caveats, we believe that the BII used in this study is robust enough to demonstrate the overall loss of biodiversity intactness across global countries worldwide over the past two decades. Additionally, it highlights the contrasting inequality in the BII trend between areas with low and high levels of biodiversity. These findings underscore the importance of prioritizing sustainable practices and behavior to mitigate biodiversity disparity and achieve sustainable development goals.

5. Conclusions

This study quantified the spatial heterogeneity of global biodiversity integrity and its association with climate factors by integrating BII time series data from 2000 to 2020, providing a scientific basis for formulating differentiated conservation strategies. The study found the following: (1) Pronounced baseline heterogeneity. Global BII magnitude (mean 0.76 ± 0.16) exhibits strong latitudinal and climatic gradients, with highest values in minimally disturbed regions (e.g., polar zones: 0.84 ± 0.06) and critically low levels (BII < 0.50) in 6% of countries, predominantly densely populated areas. (2) Divergent trends tied to baseline status. Regions with low initial biodiversity (BII < 0.50) show recovery potential (−0.007 ± 0.021 decade⁻¹), while high-biodiversity areas (BII > 0.90) face accelerated declines (0.002 ± 0.012 decade⁻¹), indicating systemic erosion of intact ecosystems. (3) Continental and climatic disparities. Europe exhibits stabilizing trends (+0.009 ± 0.017 decade⁻¹) linked to conservation efforts, whereas tropical zones experience sharp deterioration (−0.012 ± 0.022 decade⁻¹). Polar regions, despite high baseline BII (0.84 ± 0.06), show emerging negative trends. (4) Climate drivers of decline. Bioclimatic variables—particularly precipitation seasonality (BIO15, r = −0.33) and cold-quarter temperature (BIO11, r = −0.31)—strongly correlate with biodiversity loss trends but minimally influence baseline BII magnitude, highlighting climate instability as an emerging stressor.

Future research should prioritize multi-scale validation to resolve discrepancies between national-scale BII trends and local biodiversity dynamics, integrate species-level threat metrics (e.g., IUCN STAR) with ecosystem integrity indices, and establish causal linkages between BII thresholds and ecosystem service collapse risks to inform targeted planetary boundary management.